# load("vcomball20210902.Rda")
# d <- vcomball
load(path(here::here("InitalDataCleaning/Data/vsurvall20210902.Rda")))
d <- vsurvall

#load("vsiteid20210601.Rda")
new.d <- data.frame(matrix(ncol=0, nrow=nrow(d)))
new.d.1 <- data.frame(matrix(ncol=0, nrow=nrow(d)))

SITE ID

  • Codes(based on Surveyid)
    • 10 Greater CA
    • 20 Georgia
    • 25 North Carolina
    • 30 Northern CA
    • 40 Louisiana
    • 50 New Jersey
    • 60 Detroit
    • 61 Michigan
    • 70 Texas
    • 80 Los Angeles County
    • 81 USC-Other
    • 82 USC-MEC
    • 90 New York
    • 94 Florida
    • 95 WebRecruit-Limbo
    • 99 WebRecruit
  siteid <- as.factor(trimws(d[,"siteid"]))
  #new.d.n <- data.frame(new.d.n, siteid) # keep NAACCR coding
  
  levels(siteid)[levels(siteid)=="80"] <- "Los Angeles County.80"
  levels(siteid)[levels(siteid)=="30"] <- "Northern CA.30"
  levels(siteid)[levels(siteid)=="10"] <- "Greater CA.10"
  levels(siteid)[levels(siteid)=="60"] <- "Detroit.60"
  levels(siteid)[levels(siteid)=="40"] <- "Louisiana.40"
  levels(siteid)[levels(siteid)=="20"] <- "Georgia.20"
  levels(siteid)[levels(siteid)=="61"] <- "Michigan.61"
  levels(siteid)[levels(siteid)=="50"] <- "New Jersey.50"
  levels(siteid)[levels(siteid)=="70"] <- "Texas.70"
  levels(siteid)[levels(siteid)=="99"] <- "WebRecruit.99"
  levels(siteid)[levels(siteid)=="21"] <- "Georgia.21"
  levels(siteid)[levels(siteid)=="81"] <- "USC Other.81"
  levels(siteid)[levels(siteid)=="82"] <- "USC MEC.82"

  siteid_new<- siteid
  d<-data.frame(d, siteid_new)
  new.d <- data.frame(new.d, siteid)
  new.d <- apply_labels(new.d, siteid = "Site ID")
  new.d.1 <- data.frame(new.d.1, siteid)
  siteid_count<-count(new.d$siteid)
  colnames(siteid_count)<- c("Registry", "Total")
  kable(siteid_count, format = "simple", align = 'l', caption = "Overview of all Registries")
Overview of all Registries
Registry Total
Greater CA.10 409
Georgia.20 2231
Northern CA.30 258
Louisiana.40 728
New Jersey.50 383
Detroit.60 474
Michigan.61 115
Texas.70 291
Los Angeles County.80 463
USC Other.81 207
USC MEC.82 74
WebRecruit.99 206 
d<-d[which(d$siteid_new == params$site),]
new.d <- data.frame(matrix(ncol=0, nrow=nrow(d)))
#new.d<-new.d[which(new.d$siteid == params$site),]

SURVEY ID

  • Scantron assigned SurveyID
  surveyid <- as.factor(d[,"surveyid"])
  isDup <- duplicated(surveyid)
  numDups <- sum(isDup)
  dups <- surveyid[isDup]
  
  new.d <- data.frame(new.d, surveyid)
  new.d <- apply_labels(new.d, surveyid = "Survey ID")
  
  print(paste("Number of duplicates:", numDups))
## [1] "Number of duplicates: 11"
  print("The following are duplicated IDs:")
## [1] "The following are duplicated IDs:"
  print(dups)
##  [1] 200312  201605  101079  211392  990607  300631  990384  209179  600984  100849  601287 
## 5828 Levels: 100037  100050  100059  100061  100064  100072  100073  100078  100080  100084  100088  100092  ... 991829
  print("Number of NAs:")
## [1] "Number of NAs:"
  print(sum(is.na(new.d$surveyid)))
## [1] 0

LOCATION NAME

  • Name of Registry delivery location
  locationname <- as.factor(d[,"locationname"])
  
  new.d <- data.frame(new.d, locationname)
  new.d <- apply_labels(new.d, locationname = "Recruitment Location")
  temp.d <- data.frame (new.d, locationname)

  result<-questionr::freq(temp.d$locationname, total = TRUE)
  #Create a NICE table
  kable(result, format = "simple", align = 'l', caption = "Overview of Registry delivery location")
Overview of Registry delivery location
n % val%
Detroit 486 8.3 8.3
Georgia 2360 40.4 40.4
Greater Bay 258 4.4 4.4
Greater California 411 7.0 7.0
Los Angeles 463 7.9 7.9
Louisiana 599 10.3 10.3
New Jersey 383 6.6 6.6
Texas 291 5.0 5.0
Virtual 588 10.1 10.1
Total 5839 100.0 100.0

RESPOND ID

  • From Barcode label put on last page of survey by registries, identifies participant. ResponseID is assigned by the registries.
  respondid <- as.factor(d[,"respondid"])
  #remove NAs in respondid in order to avoid showing NAs in duplicated values
  respondid_rm<-respondid[!is.na(respondid)]
  isDup <- duplicated(respondid_rm)
  numDups <- sum(isDup)
  dups <- respondid_rm[isDup]
  
  new.d <- data.frame(new.d, respondid)
  new.d <- apply_labels(new.d, respondid = "RESPOND ID")
  
  print(paste("Number of duplicates:", numDups))
## [1] "Number of duplicates: 9"
  print("The following are duplicated IDs:")
## [1] "The following are duplicated IDs:"
  print(dups)
## [1] 30100172 20100647 20100647 10101033 30100177 61100327 40102138 81100245 81100244
## 4676 Levels: 10100003 10100012 10100023 10100024 10100027 10100042 10100047 10100048 10100052 10100054 ... 99800213
  print("Number of NAs:")
## [1] "Number of NAs:"
  print(sum(is.na(new.d$respondid)))
## [1] 1154

METHODOLOGY

  • How survey was completed
    • P=Paper
    • O=Online complete
st_css()
  methodology <- as.factor(d[,"methodology"])
  levels(methodology) <- list(Paper="P",
                              Online="O")
  methodology <- ordered(methodology, c("Paper", "Online"))
  new.d <- data.frame(new.d, methodology)
  new.d <- apply_labels(new.d, methodology = "Methodology for Survey Completion")
  temp.d <- data.frame (new.d, methodology)  
  
  result<-questionr::freq(temp.d$methodology, total = TRUE)
  kable(result, format = "simple", align = 'l')
n % val%
Paper 4728 81 81
Online 1111 19 19
Total 5839 100 100

A1: Date of diagnosis

  • A1. In what month and year were you first diagnosed with prostate cancer?
# a1month
a1month <- as.factor(d[,"a1month"])
  
  new.d <- data.frame(new.d, a1month)
  new.d <- apply_labels(new.d, a1month = "Month Diagnosed")
  temp.d <- data.frame (new.d, a1month) 
  
  result<-questionr::freq(temp.d$a1month, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "A1:month diagnosed")
A1:month diagnosed
n % val%
0 2 0.0 0.0
0* 1 0.0 0.0
1 361 6.2 7.1
10 441 7.6 8.6
11 361 6.2 7.1
12 275 4.7 5.4
18 1 0.0 0.0
2 377 6.5 7.4
22 1 0.0 0.0
25 2 0.0 0.0
3 499 8.5 9.8
32 1 0.0 0.0
4 445 7.6 8.7
48 1 0.0 0.0
5 444 7.6 8.7
6 655 11.2 12.8
7 409 7.0 8.0
8 406 7.0 8.0
9 420 7.2 8.2
NA 737 12.6 NA
Total 5839 100.0 100.0 
  #count<-as.data.frame(table(new.d$a1month))
  #colnames(count)<- c("a1month", "Total")
  #freq1<-table(new.d$a1month)
  #freq<-as.data.frame(round(prop.table(freq1),3))
  #colnames(freq)<- c("a1month", "Freq")
  #result<-merge(count, freq,by="a1month",sort=F)
  #kable(result, format = "simple", align = 'l', caption = "A1:month diagnosed")

#a1year
  tmp<-d[,"a1year"]
  tmp[tmp=="15"]<-"2015"
  a1year <- as.factor(tmp)
  #levels(a1year)[levels(a1year)=="15"] <- "2015"
  #a1year[a1year=="15"] <- "2015"  # change "15" to "2015"
  #a1year <- as.Date(a1year, format = "%Y")
  #a1year <- relevel(a1year, ref="1914")

  new.d <- data.frame(new.d, a1year)
  new.d <- apply_labels(new.d, a1year = "Year Diagnosed")
  temp.d <- data.frame (new.d, a1year) 

  result<-questionr::freq(temp.d$a1year, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "A1:year diagnosed")
A1:year diagnosed
n % val%
19 1 0.0 0.0
1914 1 0.0 0.0
1915 5 0.1 0.1
1916 9 0.2 0.2
1917 12 0.2 0.2
1918 3 0.1 0.1
1919 1 0.0 0.0
1920 1 0.0 0.0
1936 1 0.0 0.0
1937 1 0.0 0.0
1941 4 0.1 0.1
1943 2 0.0 0.0
1944 1 0.0 0.0
1945 2 0.0 0.0
1946 1 0.0 0.0
1947 2 0.0 0.0
1948 2 0.0 0.0
1949 3 0.1 0.1
1950 4 0.1 0.1
1951 3 0.1 0.1
1952 3 0.1 0.1
1953 1 0.0 0.0
1954 3 0.1 0.1
1955 2 0.0 0.0
1956 2 0.0 0.0
1957 1 0.0 0.0
1960 3 0.1 0.1
1963 2 0.0 0.0
1965 1 0.0 0.0
1980 1 0.0 0.0
1985 1 0.0 0.0
1987 1 0.0 0.0
1988 1 0.0 0.0
1989 1 0.0 0.0
1990 2 0.0 0.0
1992 1 0.0 0.0
1993 5 0.1 0.1
1994 2 0.0 0.0
1995 6 0.1 0.1
1996 3 0.1 0.1
1997 4 0.1 0.1
1998 9 0.2 0.2
1999 15 0.3 0.3
20 2 0.0 0.0
2000 8 0.1 0.1
2001 2 0.0 0.0
2002 2 0.0 0.0
2003 4 0.1 0.1
2004 14 0.2 0.3
2005 23 0.4 0.4
2006 8 0.1 0.1
2007 8 0.1 0.1
2008 14 0.2 0.3
2009 22 0.4 0.4
2010 75 1.3 1.4
2011 64 1.1 1.2
2012 97 1.7 1.8
2013 129 2.2 2.4
2014 267 4.6 5.0
2015 1139 19.5 21.2
2016 1593 27.3 29.6
2017 1089 18.7 20.3
2018 480 8.2 8.9
2019 141 2.4 2.6
2020 51 0.9 0.9
2021 13 0.2 0.2
3017 1 0.0 0.0
615 1 0.0 0.0
618 1 0.0 0.0
NA 462 7.9 NA
Total 5839 100.0 100.0 
  #a1not
# 1=I have NEVER had prostate cancer
# 2=I HAVE or HAVE HAD prostate cancer
# (paper survey only had a bubble for “never had” so value set to 2 if bubble not marked)"
  a1not <- as.factor(d[,"a1not"])
  levels(a1not) <- list(NEVER_had_ProstateCancer="1",
                         HAVE_had_ProstateCancer="2")
  new.d <- data.frame(new.d, a1not)
  new.d <- apply_labels(new.d, a1not = "Not Diagnosed")
  temp.d <- data.frame (new.d, a1not) 

  result<-questionr::freq(temp.d$a1not, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "A1:not diagnosed")
A1:not diagnosed
n % val%
NEVER_had_ProstateCancer 18 0.3 0.3
HAVE_had_ProstateCancer 5821 99.7 99.7
Total 5839 100.0 100.0

A2: Identify as AA

  • A2. Do you identify as Black or African American?
    • 2=Yes
    • 1=No
a2 <- as.factor(d[,"a2"])
# Make "*" to NA
a2[which(a2=="*")]<-"NA"
levels(a2) <- list(No="1",
                   Yes="2")
  a2 <- ordered(a2, c("Yes","No"))
  
  new.d <- data.frame(new.d, a2)
  new.d <- apply_labels(new.d, a2 = "Month Diagnosed")
  temp.d <- data.frame (new.d, a2) 
  
  result<-questionr::freq(temp.d$a2, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "A2")
A2
n % val%
Yes 5389 92.3 99.7
No 18 0.3 0.3
NA 432 7.4 NA
Total 5839 100.0 100.0

A3: Black or African American group

  • A3. If Yes: A2. Which Black or African American group(s) and other races/ethnicities do you identify with? Mark all that apply.
    • A3_1: 1=Black/African American
    • A3_2: 1=Nigerian
    • A3_3: 1=Jamaican
    • A3_4: 1=Ethiopian
    • A3_5: 1=Haitian
    • A3_6: 1=Somali
    • a3_7: 1=Guyanese
    • A3_8: 1=Creole
    • A3_9: 1=West Indian
    • A3_10: 1=Caribbean
    • A3_11: 1=White
    • A3_12: 1=Asian/Asian American
    • A3_13: 1=Native American or American Indian or Alaskan Native
    • A3_14: 1=Middle Eastern or North African
    • A3_15: 1=Native Hawaiian or Pacific Islander
    • A3_16: 1=Hispanic
    • A3_17: 1=Latino
    • A3_18: 1=Spanish
    • A3_19: 1=Mexican/Mexican American
    • A3_20: 1=Salvadoran
    • A3_21: 1=Puerto Rican
    • A3_22: 1=Dominican
    • A3_23: 1=Columbian
    • A3_24: 1=Other
a3_1 <- as.factor(d[,"a3_1"])
  levels(a3_1) <- list(Black_African_American="1")
  new.d <- data.frame(new.d, a3_1)
  new.d <- apply_labels(new.d, a3_1 = "Black_African_American")
  temp.d <- data.frame (new.d, a3_1)
  result<-questionr::freq(temp.d$a3_1, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Black_African_American")
1. Black_African_American
n % val%
Black_African_American 5372 92 100
NA 467 8 NA
Total 5839 100 100 
a3_2 <- as.factor(d[,"a3_2"])
  levels(a3_2) <- list(Nigerian="1")
  new.d <- data.frame(new.d, a3_2)
  new.d <- apply_labels(new.d, a3_2 = "Nigerian")
  temp.d <- data.frame (new.d, a3_2)
  result<-questionr::freq(temp.d$a3_2, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Nigerian")
2. Nigerian
n % val%
Nigerian 122 2.1 100
NA 5717 97.9 NA
Total 5839 100.0 100 
a3_3 <- as.factor(d[,"a3_3"])
  levels(a3_3) <- list(Jamaican="1")
  new.d <- data.frame(new.d, a3_3)
  new.d <- apply_labels(new.d, a3_3 = "Jamaican")
  temp.d <- data.frame (new.d, a3_3)
  result<-questionr::freq(temp.d$a3_3, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Jamaican")
3. Jamaican
n % val%
Jamaican 116 2 100
NA 5723 98 NA
Total 5839 100 100 
a3_4 <- as.factor(d[,"a3_4"])
  levels(a3_4) <- list(Ethiopian="1")
  new.d <- data.frame(new.d, a3_4)
  new.d <- apply_labels(new.d, a3_4 = "Ethiopian")
  temp.d <- data.frame (new.d, a3_4)
  result<-questionr::freq(temp.d$a3_4, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "4. Ethiopian")
4. Ethiopian
n % val%
Ethiopian 16 0.3 100
NA 5823 99.7 NA
Total 5839 100.0 100 
a3_5 <- as.factor(d[,"a3_5"])
  levels(a3_5) <- list(Haitian="1")
  new.d <- data.frame(new.d, a3_5)
  new.d <- apply_labels(new.d, a3_5 = "Haitian")
  temp.d <- data.frame (new.d, a3_5)
  result<-questionr::freq(temp.d$a3_5, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "5. Haitian")
5. Haitian
n % val%
Haitian 39 0.7 100
NA 5800 99.3 NA
Total 5839 100.0 100 
a3_6 <- as.factor(d[,"a3_6"])
  levels(a3_6) <- list(Somali="1")
  new.d <- data.frame(new.d, a3_6)
  new.d <- apply_labels(new.d, a3_6 = "Somali")
  temp.d <- data.frame (new.d, a3_6)
  result<-questionr::freq(temp.d$a3_6, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "6. Somali")
6. Somali
n % val%
Somali 3 0.1 100
NA 5836 99.9 NA
Total 5839 100.0 100 
a3_7 <- as.factor(d[,"a3_7"])
  levels(a3_7) <- list(Guyanese="1")
  new.d <- data.frame(new.d, a3_7)
  new.d <- apply_labels(new.d, a3_7 = "Guyanese")
  temp.d <- data.frame (new.d, a3_7)
  result<-questionr::freq(temp.d$a3_7, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "7. Guyanese")
7. Guyanese
n % val%
Guyanese 28 0.5 100
NA 5811 99.5 NA
Total 5839 100.0 100 
a3_8 <- as.factor(d[,"a3_8"])
  levels(a3_8) <- list(Creole="1")
  new.d <- data.frame(new.d, a3_8)
  new.d <- apply_labels(new.d, a3_8 = "Creole")
  temp.d <- data.frame (new.d, a3_8)
  result<-questionr::freq(temp.d$a3_8, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "8. Creole")
8. Creole
n % val%
Creole 69 1.2 100
NA 5770 98.8 NA
Total 5839 100.0 100 
a3_9 <- as.factor(d[,"a3_9"])
  levels(a3_9) <- list(West_Indian="1")
  new.d <- data.frame(new.d, a3_9)
  new.d <- apply_labels(new.d, a3_9 = "West_Indian")
  temp.d <- data.frame (new.d, a3_9)
  result<-questionr::freq(temp.d$a3_9, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "9. West_Indian")
9. West_Indian
n % val%
West_Indian 110 1.9 100
NA 5729 98.1 NA
Total 5839 100.0 100 
a3_10 <- as.factor(d[,"a3_10"])
  levels(a3_10) <- list(Caribbean="1")
  new.d <- data.frame(new.d, a3_10)
  new.d <- apply_labels(new.d, a3_10 = "Caribbean")
  temp.d <- data.frame (new.d, a3_10)
  result<-questionr::freq(temp.d$a3_10, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "10. Caribbean")
10. Caribbean
n % val%
Caribbean 104 1.8 100
NA 5735 98.2 NA
Total 5839 100.0 100 
a3_11 <- as.factor(d[,"a3_11"])
  levels(a3_11) <- list(White="1")
  new.d <- data.frame(new.d, a3_11)
  new.d <- apply_labels(new.d, a3_11 = "White")
  temp.d <- data.frame (new.d, a3_11)
  result<-questionr::freq(temp.d$a3_11, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "11. White")
11. White
n % val%
White 69 1.2 100
NA 5770 98.8 NA
Total 5839 100.0 100 
a3_12 <- as.factor(d[,"a3_12"])
  levels(a3_12) <- list(Asian="1")
  new.d <- data.frame(new.d, a3_12)
  new.d <- apply_labels(new.d, a3_12 = "Asian")
  temp.d <- data.frame (new.d, a3_12)
  result<-questionr::freq(temp.d$a3_12, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "12. Asian")
12. Asian
n % val%
Asian 10 0.2 100
NA 5829 99.8 NA
Total 5839 100.0 100 
a3_13 <- as.factor(d[,"a3_13"])
  levels(a3_13) <- list(Native_Indian="1")
  new.d <- data.frame(new.d, a3_13)
  new.d <- apply_labels(new.d, a3_13 = "Native_Indian")
  temp.d <- data.frame (new.d, a3_13)
  result<-questionr::freq(temp.d$a3_13, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "13. Native_Indian")
13. Native_Indian
n % val%
Native_Indian 142 2.4 100
NA 5697 97.6 NA
Total 5839 100.0 100 
a3_14 <- as.factor(d[,"a3_14"])
  levels(a3_14) <- list(Middle_Eastern_North_African="1")
  new.d <- data.frame(new.d, a3_14)
  new.d <- apply_labels(new.d, a3_14 = "Middle_Eastern_North_African")
  temp.d <- data.frame (new.d, a3_14)
  result<-questionr::freq(temp.d$a3_14, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "14. Middle_Eastern_North_African")
14. Middle_Eastern_North_African
n % val%
Middle_Eastern_North_African 7 0.1 100
NA 5832 99.9 NA
Total 5839 100.0 100 
a3_15 <- as.factor(d[,"a3_15"])
  levels(a3_15) <- list(Native_Hawaiian_Pacific_Islander="1")
  new.d <- data.frame(new.d, a3_15)
  new.d <- apply_labels(new.d, a3_15 = "Native_Hawaiian_Pacific_Islander")
  temp.d <- data.frame (new.d, a3_15)
  result<-questionr::freq(temp.d$a3_15, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "15. Native_Hawaiian_Pacific_Islander")
15. Native_Hawaiian_Pacific_Islander
n % val%
Native_Hawaiian_Pacific_Islander 7 0.1 100
NA 5832 99.9 NA
Total 5839 100.0 100 
a3_16 <- as.factor(d[,"a3_16"])
  levels(a3_16) <- list(Hispanic="1")
  new.d <- data.frame(new.d, a3_16)
  new.d <- apply_labels(new.d, a3_16 = "Hispanic")
  temp.d <- data.frame (new.d, a3_16)
  result<-questionr::freq(temp.d$a3_16, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "16. Hispanic")
16. Hispanic
n % val%
Hispanic 24 0.4 100
NA 5815 99.6 NA
Total 5839 100.0 100 
a3_17 <- as.factor(d[,"a3_17"])
  levels(a3_17) <- list(Latino="1")
  new.d <- data.frame(new.d, a3_17)
  new.d <- apply_labels(new.d, a3_17 = "Latino")
  temp.d <- data.frame (new.d, a3_17)
  result<-questionr::freq(temp.d$a3_17, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "17. Latino")
17. Latino
n % val%
Latino 16 0.3 100
NA 5823 99.7 NA
Total 5839 100.0 100 
a3_18 <- as.factor(d[,"a3_18"])
  levels(a3_18) <- list(Spanish="1")
  new.d <- data.frame(new.d, a3_18)
  new.d <- apply_labels(new.d, a3_18 = "Spanish")
  temp.d <- data.frame (new.d, a3_18)
  result<-questionr::freq(temp.d$a3_18, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "18. Spanish")
18. Spanish
n % val%
Spanish 9 0.2 100
NA 5830 99.8 NA
Total 5839 100.0 100 
a3_19 <- as.factor(d[,"a3_19"])
  levels(a3_19) <- list(Mexican="1")
  new.d <- data.frame(new.d, a3_19)
  new.d <- apply_labels(new.d, a3_19 = "Mexican")
  temp.d <- data.frame (new.d, a3_19)
  result<-questionr::freq(temp.d$a3_19, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "19. Mexican")
19. Mexican
n % val%
Mexican 6 0.1 100
NA 5833 99.9 NA
Total 5839 100.0 100 
a3_20 <- as.factor(d[,"a3_20"])
  levels(a3_20) <- list(Salvadoran="1")
  new.d <- data.frame(new.d, a3_20)
  new.d <- apply_labels(new.d, a3_20 = "Salvadoran")
  temp.d <- data.frame (new.d, a3_20)
  result<-questionr::freq(temp.d$a3_20, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "20. Salvadoran")
20. Salvadoran
n % val%
Salvadoran 2 0 100
NA 5837 100 NA
Total 5839 100 100 
a3_21 <- as.factor(d[,"a3_21"])
  levels(a3_21) <- list(Puerto_Rican="1")
  new.d <- data.frame(new.d, a3_21)
  new.d <- apply_labels(new.d, a3_21 = "Puerto_Rican")
  temp.d <- data.frame (new.d, a3_21)
  result<-questionr::freq(temp.d$a3_21, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "21. Puerto_Rican")
21. Puerto_Rican
n % val%
Puerto_Rican 17 0.3 100
NA 5822 99.7 NA
Total 5839 100.0 100 
a3_22 <- as.factor(d[,"a3_22"])
  levels(a3_22) <- list(Dominican="1")
  new.d <- data.frame(new.d, a3_22)
  new.d <- apply_labels(new.d, a3_22 = "Dominican")
  temp.d <- data.frame (new.d, a3_22)
  result<-questionr::freq(temp.d$a3_22, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "22. Dominican")
22. Dominican
n % val%
Dominican 7 0.1 100
NA 5832 99.9 NA
Total 5839 100.0 100 
a3_23 <- as.factor(d[,"a3_23"])
  levels(a3_23) <- list(Columbian="1")
  new.d <- data.frame(new.d, a3_23)
  new.d <- apply_labels(new.d, a3_23 = "Columbian")
  temp.d <- data.frame (new.d, a3_23)
  result<-questionr::freq(temp.d$a3_23, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "23. Columbian")
23. Columbian
n % val%
Columbian 4 0.1 100
NA 5835 99.9 NA
Total 5839 100.0 100 
a3_24 <- as.factor(d[,"a3_24"])
  levels(a3_23) <- list(Other="1")
  new.d <- data.frame(new.d, a3_24)
  new.d <- apply_labels(new.d, a3_24 = "Other")
  temp.d <- data.frame (new.d, a3_24)
  result<-questionr::freq(temp.d$a3_24, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "24. Other")
24. Other
n % val%
1 94 1.6 100
NA 5745 98.4 NA
Total 5839 100.0 100

A3 Other: Black or African American group

a3other <- d[,"a3other"]
  new.d <- data.frame(new.d, a3other)
  new.d <- apply_labels(new.d, a3other = "A3Other")
  temp.d <- data.frame (new.d, a3other)
result<-questionr::freq(temp.d$a3other, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "A3Other")
A3Other
n % val%
???!!! 1 0.0 0.7
Aboriginal 1 0.0 0.7
African (Liberian) 1 0.0 0.7
All black American 1 0.0 0.7
American 1 0.0 0.7
American Black 1 0.0 0.7
american with african and european heritage 1 0.0 0.7
American-I know no other 1 0.0 0.7
American. 1 0.0 0.7
Asian Indian 1 0.0 0.7
Atlantic Islander, British, Bermuda 1 0.0 0.7
Bahamas 1 0.0 0.7
Barbadian 1 0.0 0.7
Barbadion My birth place 1 0.0 0.7
Barbados 1 0.0 0.7
Because my grandparents were mixed with Spanish and Native American and black. 1 0.0 0.7
Belize 1 0.0 0.7
Belize Central America, mother Hondurian 1 0.0 0.7
Belizean 1 0.0 0.7
Black 1 0.0 0.7
black american 1 0.0 0.7
Black British 1 0.0 0.7
Black Jew/Negro 1 0.0 0.7
Black Latino. 1 0.0 0.7
Black Man 1 0.0 0.7
Black, white , American Indian 1 0.0 0.7
Blackfoot Indian 1 0.0 0.7
Blended mix race 1 0.0 0.7
Born in St. Thomas US Virgin Island 1 0.0 0.7
Both African-American and Creole. 1 0.0 0.7
Cameroon 1 0.0 0.7
Cameroon, Congo 1 0.0 0.7
Cameroon, Congo (from ethnicity estimate) from DNA sample 1 0.0 0.7
Cameroonian 1 0.0 0.7
Cameroonian. 2 0.0 1.5
Cape Verdean 1 0.0 0.7
Chanaian 1 0.0 0.7
Congolese 2 0.0 1.5
Cuban 2 0.0 1.5
English 1 0.0 0.7
Father Panamanian 1 0.0 0.7
Fathers family 1 0.0 0.7
French 1 0.0 0.7
French and Black and Cherokee 1 0.0 0.7
From West Africa, Ivory Coast (French Speaking) 1 0.0 0.7
Gambian 1 0.0 0.7
German/Jewish 1 0.0 0.7
Ghana 2 0.0 1.5
Ghana. 2 0.0 1.5
Ghanaian 8 0.1 6.0
Ghanaian. 6 0.1 4.5
Ghanaian/US citizen 1 0.0 0.7
Ghanian 1 0.0 0.7
Great Grand Father Side (Kenyon) 1 0.0 0.7
Greek 1 0.0 0.7
Guatemalan 1 0.0 0.7
Guinea-CKY 1 0.0 0.7
Guyanese 1 0.0 0.7
Haitian 1 0.0 0.7
Hebrew Israelite Black 1 0.0 0.7
Hebrew Israelite. 1 0.0 0.7
Human race 2 0.0 1.5
I do have fore parents from the African continent I am, however I am not familiar with the name or country of origin unfamiliar 1 0.0 0.7
I’m biracial, Father-white, mother-black. 1 0.0 0.7
If you need to call me Larry D. Joyner 912-344-5895. 1 0.0 0.7
India/South Asia 1 0.0 0.7
Indigenous Moor. 1 0.0 0.7
Irish 1 0.0 0.7
Irish/Cape Verdian 1 0.0 0.7
Jewish 2 0.0 1.5
Kenyan 2 0.0 1.5
Liberian African 1 0.0 0.7
Liberian. 1 0.0 0.7
Louisiana Creole of color, French 1 0.0 0.7
Mixed Black/White 1 0.0 0.7
Mixed race - White & American Black 1 0.0 0.7
Moorish National Indigenous to 1 0.0 0.7
Mother 1/2 White 1 0.0 0.7
Mother is white and Father was Black 1 0.0 0.7
Mulatto, Latino and African 1 0.0 0.7
My choice would be American 1 0.0 0.7
My father dad was Jewish 1 0.0 0.7
Negro 3 0.1 2.2
None. 2 0.0 1.5
of mixed heritage 1 0.0 0.7
Panama is also a country! 1 0.0 0.7
Panamanian 1 0.0 0.7
Panamanian/Costa Rican 1 0.0 0.7
Parents of color Jamaican father, mother Cuban 1 0.0 0.7
Rwandan 1 0.0 0.7
Sex problems and smaller penis 1 0.0 0.7
Sierra Leone 1 0.0 0.7
Sierra Leonean 2 0.0 1.5
Sierra Leonean, West Africa 1 0.0 0.7
Sierre Leone 1 0.0 0.7
Surnam African American. 1 0.0 0.7
Tanzanian 1 0.0 0.7
Togo 1 0.0 0.7
Togolese 1 0.0 0.7
Trinidad 1 0.0 0.7
Trinidad and Tobago. 1 0.0 0.7
Trinidadian 1 0.0 0.7
Trinidadian. 1 0.0 0.7
Uganda 1 0.0 0.7
Venezuela 1 0.0 0.7
Virgin Islands 1 0.0 0.7
West Africa Liberia 1 0.0 0.7
West African 3 0.1 2.2
NA 5705 97.7 NA
Total 5839 100.0 100.0

A4: Month and year of birth

A4. What is your month and year of birth?

# a4month
a4month <- as.factor(d[,"a4month"])
  new.d <- data.frame(new.d, a4month)
  new.d <- apply_labels(new.d, a4month = "Month of birth")
  temp.d <- data.frame (new.d, a4month) 
  
  result<-questionr::freq(temp.d$a4month, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "A4: Month of birth")
A4: Month of birth
n % val%
1 486 8.3 8.4
10 515 8.8 8.9
11 478 8.2 8.2
12 520 8.9 9.0
18 1 0.0 0.0
2 441 7.6 7.6
22 1 0.0 0.0
24 2 0.0 0.0
25 1 0.0 0.0
26 1 0.0 0.0
3 451 7.7 7.8
31 1 0.0 0.0
33 1 0.0 0.0
35 1 0.0 0.0
4 417 7.1 7.2
41 2 0.0 0.0
42 1 0.0 0.0
48 1 0.0 0.0
5 444 7.6 7.6
57 1 0.0 0.0
58 1 0.0 0.0
6 463 7.9 8.0
61 2 0.0 0.0
7 516 8.8 8.9
71 2 0.0 0.0
8 555 9.5 9.6
84 1 0.0 0.0
9 503 8.6 8.7
96 1 0.0 0.0
NA 29 0.5 NA
Total 5839 100.0 100.0 
#a4year
a4year <- as.factor(d[,"a4year"])
  new.d <- data.frame(new.d, a4year)
  new.d <- apply_labels(new.d, a4year = "Year of birth")
  temp.d <- data.frame (new.d, a4year) 

  result<-questionr::freq(temp.d$a4year, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "A4: Year of birth")
A4: Year of birth
n % val%
1062 1 0.0 0.0
1340 1 0.0 0.0
1923 1 0.0 0.0
1924 2 0.0 0.0
1925 1 0.0 0.0
1927 1 0.0 0.0
1928 2 0.0 0.0
1929 1 0.0 0.0
1930 1 0.0 0.0
1931 2 0.0 0.0
1932 5 0.1 0.1
1933 5 0.1 0.1
1934 5 0.1 0.1
1935 8 0.1 0.1
1936 9 0.2 0.2
1937 19 0.3 0.3
1938 24 0.4 0.4
1939 31 0.5 0.5
1940 43 0.7 0.7
1941 90 1.5 1.5
1942 108 1.8 1.9
1943 145 2.5 2.5
1944 153 2.6 2.6
1945 170 2.9 2.9
1946 237 4.1 4.1
1947 240 4.1 4.1
1948 306 5.2 5.3
1949 299 5.1 5.1
1950 320 5.5 5.5
1951 322 5.5 5.5
1952 292 5.0 5.0
1953 282 4.8 4.8
1954 293 5.0 5.0
1955 295 5.1 5.1
1956 299 5.1 5.1
1957 277 4.7 4.8
1958 235 4.0 4.0
1959 192 3.3 3.3
1960 222 3.8 3.8
1961 171 2.9 2.9
1962 130 2.2 2.2
1963 128 2.2 2.2
1964 94 1.6 1.6
1965 75 1.3 1.3
1966 68 1.2 1.2
1967 55 0.9 0.9
1968 45 0.8 0.8
1969 37 0.6 0.6
1970 17 0.3 0.3
1971 12 0.2 0.2
1972 10 0.2 0.2
1973 15 0.3 0.3
1974 3 0.1 0.1
1975 3 0.1 0.1
1976 2 0.0 0.0
1977 2 0.0 0.0
1978 1 0.0 0.0
1981 1 0.0 0.0
2015 3 0.1 0.1
2016 1 0.0 0.0
2018 2 0.0 0.0
2582 1 0.0 0.0
2819 1 0.0 0.0
3042 1 0.0 0.0
663 1 0.0 0.0
748 1 0.0 0.0
NA 20 0.3 NA
Total 5839 100.0 100.0

A5: Where were you born

  • A5. Where were you born?
    • 1=United States (includes Hawaii and US territories)
    • 2=Africa
    • 3=Cuba or Caribbean Islands
    • 4=Other
a5 <- as.factor(d[,"a5"])
# Make "*" to NA
a5[which(a5=="*")]<-"NA"
levels(a5) <- list(US="1",
                   Africa="2",
                   Cuba_Caribbean= "3",
                   Other="4")
  a5 <- ordered(a5, c("US","Africa","Cuba_Caribbean","Other"))
  
  new.d <- data.frame(new.d, a5)
  new.d <- apply_labels(new.d, a5 = "Born place")
  temp.d <- data.frame (new.d, a5) 
  
  result<-questionr::freq(temp.d$a5, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "A5: Where were you born?")
A5: Where were you born?
n % val%
US 5369 92.0 92.5
Africa 181 3.1 3.1
Cuba_Caribbean 156 2.7 2.7
Other 99 1.7 1.7
NA 34 0.6 NA
Total 5839 100.0 100.0

A5 Other: Where were you born

a5other <- d[,"a5other"]
  new.d <- data.frame(new.d, a5other)
  new.d <- apply_labels(new.d, a5other = "a5other")
  temp.d <- data.frame (new.d, a5other)
result<-questionr::freq(temp.d$a5other, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "A5Other")
A5Other
n % val%
Achtal, Bavaria, Germany 1 0.0 0.7
Augsburg, Germany 1 0.0 0.7
Barbados 2 0.0 1.3
Beaumont Texas 1 0.0 0.7
Belize 1 0.0 0.7
Belize Central America 1 0.0 0.7
Belize, Central America 2 0.0 1.3
Belize, Toledo District 1 0.0 0.7
Bermuda Island 1 0.0 0.7
British Guiana, South America 1 0.0 0.7
Buffalo NY 1 0.0 0.7
Central America (Panama) 1 0.0 0.7
Central America Belize 1 0.0 0.7
Chatroux, France 1 0.0 0.7
Chicago IL 1 0.0 0.7
Colon Panama 1 0.0 0.7
Costa Rica 1 0.0 0.7
Detroit 1 0.0 0.7
Dominican Republic. 1 0.0 0.7
England 1 0.0 0.7
England. 1 0.0 0.7
Ethiopia 1 0.0 0.7
France 1 0.0 0.7
Frankfort, Germany 1 0.0 0.7
Georgetown Guyana 1 0.0 0.7
Georgia Monroe Walton County 1 0.0 0.7
Georgia. 1 0.0 0.7
Germany 2 0.0 1.3
Ghana 3 0.1 2.0
Ghana Elmina West Africa 1 0.0 0.7
Ghana. 1 0.0 0.7
Guatemala 1 0.0 0.7
Guinea-CKY 1 0.0 0.7
Guyana 10 0.2 6.6
Guyana, South America 3 0.1 2.0
Guyana, South America. 1 0.0 0.7
Guyana: South America 1 0.0 0.7
Guyana. 2 0.0 1.3
Haiti 12 0.2 7.9
Haiti-P-au-P. 1 0.0 0.7
Haiti. 5 0.1 3.3
Heidleburg Germany 1 0.0 0.7
Honduras CA 1 0.0 0.7
Houston 1 0.0 0.7
Jackson Mississippi 1 0.0 0.7
Jackson, Miss. 1 0.0 0.7
Jamaica 11 0.2 7.2
JAMAICA 1 0.0 0.7
Jamaica W.I. 1 0.0 0.7
Jamaica West Indies 1 0.0 0.7
Jamaica WI 1 0.0 0.7
Jamaica, W.I.. 1 0.0 0.7
Jamaica, WI. 1 0.0 0.7
Jamaica. 1 0.0 0.7
Jamaican 1 0.0 0.7
Japan 1 0.0 0.7
Kientra, Morocco 1 0.0 0.7
Kingston, Jamaica 1 0.0 0.7
Kingston, Jamaica W.I.. 1 0.0 0.7
Kingston, Jamaica. 1 0.0 0.7
Liberia 1 0.0 0.7
London England 1 0.0 0.7
Macon County 1 0.0 0.7
Mississippi 1 0.0 0.7
Nassau Bahamas 2 0.0 1.3
New Orleans, LA 1 0.0 0.7
Nigeria 2 0.0 1.3
Nigeria. 1 0.0 0.7
P-Au-P, Haiti 1 0.0 0.7
Palm Beach Fla 1 0.0 0.7
Panama 4 0.1 2.6
Panama City of Panama 1 0.0 0.7
Panama City Panama 1 0.0 0.7
Panama Rep Panama 1 0.0 0.7
Panama. 2 0.0 1.3
Phila PA 1 0.0 0.7
Puerto Rico. 1 0.0 0.7
Rep of Panama. 1 0.0 0.7
Republic of Guyana. 1 0.0 0.7
San Diego, CA 1 0.0 0.7
SC 1 0.0 0.7
Sierre Leone 1 0.0 0.7
South America, Guyana 1 0.0 0.7
South Carolina 1 0.0 0.7
Southampton, Bermuda 1 0.0 0.7
St. Lucia 1 0.0 0.7
St. Vincent and the Grenadines. 1 0.0 0.7
Suriname-Dutch Guyana. 1 0.0 0.7
tokyo japan 1 0.0 0.7
Trinidad 2 0.0 1.3
Trinidad and Tobago 3 0.1 2.0
Trinidad. 2 0.0 1.3
UK 1 0.0 0.7
United Kingdom 1 0.0 0.7
United States Texas 1 0.0 0.7
United States. 1 0.0 0.7
Upson County, GA 1 0.0 0.7
Venezuela 1 0.0 0.7
Venezuelan 1 0.0 0.7
West Indies 1 0.0 0.7
NA 5687 97.4 NA
Total 5839 100.0 100.0

A6: Biological father born

  • A6. Where was your biological father born?
    • 1=United States (includes Hawaii and US territories)
    • 2=Africa
    • 3=Cuba or Caribbean Islands
    • 4=Other
a6 <- as.factor(d[,"a6"])
# Make "*" to NA
a6[which(a6=="*")]<-"NA"
levels(a6) <- list(US="1",
                   Africa="2",
                   Cuba_Caribbean= "3",
                   Other="4")
  a6 <- ordered(a6, c("US","Africa","Cuba_Caribbean","Other"))
  
  new.d <- data.frame(new.d, a6)
  new.d <- apply_labels(new.d, a6 = "Born place")
  temp.d <- data.frame (new.d, a6) 
  
  result<-questionr::freq(temp.d$a6, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "a6: Where were you born?")
a6: Where were you born?
n % val%
US 5286 90.5 91.5
Africa 183 3.1 3.2
Cuba_Caribbean 188 3.2 3.3
Other 117 2.0 2.0
NA 65 1.1 NA
Total 5839 100.0 100.0

A6 Other: Biological father born

a6other <- d[,"a6other"]
  new.d <- data.frame(new.d, a6other)
  new.d <- apply_labels(new.d, a6other = "a6other")
  temp.d <- data.frame (new.d, a6other)
result<-questionr::freq(temp.d$a6other, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "A6Other")
A6Other
n % val%
Bangladesh 1 0.0 0.6
Barbados 4 0.1 2.5
Belize 1 0.0 0.6
Belize Central America 2 0.0 1.2
Belize, Central America 2 0.0 1.2
Bermuda Island 1 0.0 0.6
Biological father unknown 1 0.0 0.6
Blackstone, VA 1 0.0 0.6
Bombay. 1 0.0 0.6
British Guiana 1 0.0 0.6
British Honduras. 1 0.0 0.6
Canada 1 0.0 0.6
Canada. 1 0.0 0.6
Castries St. Lucia 1 0.0 0.6
Central America (Panama) 1 0.0 0.6
Charleston South Carolina 1 0.0 0.6
Cleveland Mississippi 1 0.0 0.6
Costa Rica 1 0.0 0.6
Dead 1 0.0 0.6
Dominican Republic. 1 0.0 0.6
Don’t know 2 0.0 1.2
Don’t know biological father 1 0.0 0.6
Don’t know who father is 1 0.0 0.6
East Pakistan 1 0.0 0.6
England 1 0.0 0.6
Ethiopia 1 0.0 0.6
Father’s birthplace is unknown. 1 0.0 0.6
Georgia Monroe Walton County 1 0.0 0.6
Georgia. 1 0.0 0.6
Germany 1 0.0 0.6
Ghana 2 0.0 1.2
Ghana Elmina West Africa 1 0.0 0.6
Granada 1 0.0 0.6
Guatemala 1 0.0 0.6
Guyana 10 0.2 6.2
Guyana, South America 3 0.1 1.9
Guyana, South America. 1 0.0 0.6
Guyana: South America 1 0.0 0.6
Guyana. 2 0.0 1.2
Haiti 12 0.2 7.4
Haiti. 4 0.1 2.5
Honduras CA 1 0.0 0.6
Honduras, Central America 1 0.0 0.6
I don’t know 1 0.0 0.6
I was adopted, no info 1 0.0 0.6
Jackson Mississippi 1 0.0 0.6
Jamaica 12 0.2 7.4
JAMAICA 1 0.0 0.6
Jamaica W.I. 1 0.0 0.6
Jamaica West Indies 1 0.0 0.6
Jamaica WI 2 0.0 1.2
Jamaica, WI. 1 0.0 0.6
Jamaica. 2 0.0 1.2
Jamaica. W.I.. 1 0.0 0.6
Jamaican 1 0.0 0.6
Kingston, Jamaica, W.I.. 1 0.0 0.6
Kingston, Jamaica. 1 0.0 0.6
Limon, Costa Rica 1 0.0 0.6
Macon County 1 0.0 0.6
Marshall Texas 1 0.0 0.6
Mississippi 2 0.0 1.2
Montserrat British VI 1 0.0 0.6
Nassau Bahamas 2 0.0 1.2
Never knew my father 1 0.0 0.6
Nigeria 2 0.0 1.2
Nigeria. 1 0.0 0.6
Not known 1 0.0 0.6
not sure 1 0.0 0.6
P-Au-P, Haiti 1 0.0 0.6
Panama 4 0.1 2.5
Panama Canal Zone 1 0.0 0.6
Panama City of Panama 1 0.0 0.6
Panama City Panama 1 0.0 0.6
Panama Rep Panama 1 0.0 0.6
Panama-Central America 1 0.0 0.6
Panama. 2 0.0 1.2
Phila PA 1 0.0 0.6
Port-au-Prince, Haiti. 1 0.0 0.6
Possible Puerto Rico 1 0.0 0.6
Puerto Rico 1 0.0 0.6
Rep of Panama. 1 0.0 0.6
Republic of Guyana. 1 0.0 0.6
SC 1 0.0 0.6
Sierre Leone 1 0.0 0.6
Singapore. 1 0.0 0.6
South America, Guyana 1 0.0 0.6
St. Louis, MO 1 0.0 0.6
St. Vincent and the Grenadines. 1 0.0 0.6
St. Vincent. 1 0.0 0.6
Suriname-Dutch Guyana. 1 0.0 0.6
Trinidad 2 0.0 1.2
Trinidad and Tabago 1 0.0 0.6
Trinidad and Tobago 2 0.0 1.2
Trinidad. 1 0.0 0.6
United S Texas 1 0.0 0.6
United States. 1 0.0 0.6
unknown 2 0.0 1.2
Unknown 5 0.1 3.1
Venezuela 1 0.0 0.6
Vieques, Puerto Rico. 1 0.0 0.6
west Indies 1 0.0 0.6
Yatesville GA 1 0.0 0.6
NA 5677 97.2 NA
Total 5839 100.0 100.0

A7: Biological mother born

  • A7. Where was your biological mother born?
    • 1=United States (includes Hawaii and US territories)
    • 2=Africa
    • 3=Cuba or Caribbean Islands
    • 4=Other
a7 <- as.factor(d[,"a7"])
# Make "*" to NA
a7[which(a7=="*")]<-"NA"
levels(a7) <- list(US="1",
                   Africa="2",
                   Cuba_Caribbean= "3",
                   Other="4")
  a7 <- ordered(a7, c("US","Africa","Cuba_Caribbean","Other"))
  
  new.d <- data.frame(new.d, a7)
  new.d <- apply_labels(new.d, a7 = "Born place")
  temp.d <- data.frame (new.d, a7) 
  
  result<-questionr::freq(temp.d$a7, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "a7: Where were you born?")
a7: Where were you born?
n % val%
US 5328 91.2 92.0
Africa 180 3.1 3.1
Cuba_Caribbean 184 3.2 3.2
Other 100 1.7 1.7
NA 47 0.8 NA
Total 5839 100.0 100.0

A7 Other: Biological father born

a7other <- d[,"a7other"]
  new.d <- data.frame(new.d, a7other)
  new.d <- apply_labels(new.d, a7other = "a7other")
  temp.d <- data.frame (new.d, a7other)
result<-questionr::freq(temp.d$a7other, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "A7Other")
A7Other
n % val%
Achtal, Bavaria, Germany 1 0.0 0.7
Anse-a-veau, Haiti 1 0.0 0.7
Atlanta, GA 1 0.0 0.7
Barbados 2 0.0 1.4
Belize 1 0.0 0.7
Belize Central America 1 0.0 0.7
Belize, Central America 2 0.0 1.4
Bermuda Island 1 0.0 0.7
Bremont, TX 1 0.0 0.7
British Guiana 1 0.0 0.7
Buffalo NY 1 0.0 0.7
Canada. 2 0.0 1.4
Central America (Colon, Panama) 1 0.0 0.7
Colombia 1 0.0 0.7
Costa rica 1 0.0 0.7
Costa Rica 2 0.0 1.4
Dominican Republic. 1 0.0 0.7
England 1 0.0 0.7
Ethiopia 1 0.0 0.7
Georgia Good Hope Walton County 1 0.0 0.7
Georgia. 1 0.0 0.7
Germany. 2 0.0 1.4
Ghana 2 0.0 1.4
Ghana Elmina West Africa 1 0.0 0.7
Grenada 1 0.0 0.7
Guatemala 1 0.0 0.7
guyana 1 0.0 0.7
Guyana 11 0.2 7.6
Guyana, South America 3 0.1 2.1
Guyana, South America. 1 0.0 0.7
Guyana: South America 1 0.0 0.7
Guyana. 2 0.0 1.4
Haiti 12 0.2 8.3
Haiti. 4 0.1 2.8
Honduras CA 1 0.0 0.7
Jackson Mississippi 1 0.0 0.7
Jamaica 13 0.2 9.0
JAMAICA 1 0.0 0.7
Jamaica BWI Kingston 1 0.0 0.7
Jamaica W.I. 1 0.0 0.7
Jamaica WI 2 0.0 1.4
Jamaica, W.I.. 1 0.0 0.7
Jamaica, WI. 1 0.0 0.7
Jamaica. 1 0.0 0.7
Japan 3 0.1 2.1
Kingston, Jamaica, W.I.. 1 0.0 0.7
Kingston, Jamaica. 1 0.0 0.7
LA 1 0.0 0.7
Lamar County, GA 1 0.0 0.7
Litchfield Guyana 1 0.0 0.7
Little Rock Arkansas 1 0.0 0.7
Macon County 1 0.0 0.7
Mexico 1 0.0 0.7
Mirebalais, Haiti. 1 0.0 0.7
Mississippi 2 0.0 1.4
Montserrat British VI 1 0.0 0.7
Nassau Bahamas 1 0.0 0.7
Nigeria 2 0.0 1.4
Nigeria. 1 0.0 0.7
No biological info, I was adopted. 1 0.0 0.7
Oklahoma 1 0.0 0.7
Panama 5 0.1 3.4
Panama City of Panama 1 0.0 0.7
Panama City Panama 1 0.0 0.7
Panama Rep Panama 1 0.0 0.7
Panama. 2 0.0 1.4
Phila PA 1 0.0 0.7
Puerto Rican 1 0.0 0.7
Puerto Rico. 1 0.0 0.7
Republic of Guyana. 1 0.0 0.7
SC 1 0.0 0.7
Sierre Leone 1 0.0 0.7
South America, Guyana 1 0.0 0.7
Spanish Honduras 1 0.0 0.7
St. Lucia 1 0.0 0.7
St. Vincent and the Grenadines. 1 0.0 0.7
Suriname-Dutch Guyana. 1 0.0 0.7
Trinidad 1 0.0 0.7
Trinidad and Tobago 3 0.1 2.1
Trinidad West Indies 1 0.0 0.7
Trinidad. 2 0.0 1.4
United States Texas 1 0.0 0.7
United States. 1 0.0 0.7
Venezuela 2 0.0 1.4
Vieques, Puerto Rico. 1 0.0 0.7
west Indies 1 0.0 0.7
NA 5694 97.5 NA
Total 5839 100.0 100.0

A8: Years lived in the US

  • A8. How many years have you lived in the United States?
    • 1=15 years or less
    • 2=16-25 years
    • 3=My whole life or more than 25 years
a8 <- as.factor(d[,"a8"])
# Make "*" to NA
a8[which(a8=="*")]<-"NA"
levels(a8) <- list(less_or_15="1",
                   years_16_25="2",
                   more_than_25_or_whole_life= "3")
  a8 <- ordered(a8, c("less_or_15","years_16_25","more_than_25_or_whole_life"))
  
  new.d <- data.frame(new.d, a8)
  new.d <- apply_labels(new.d, a8 = "Years lived in the US")
  temp.d <- data.frame (new.d, a8) 
  
  result<-questionr::freq(temp.d$a8, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "A8")
A8
n % val%
less_or_15 45 0.8 0.8
years_16_25 90 1.5 1.6
more_than_25_or_whole_life 5580 95.6 97.6
NA 124 2.1 NA
Total 5839 100.0 100.0

B1A: Father

  • B1Aa: Father: Has this person had prostate cancer?
  • B1Ab: Father: Was he (or any) diagnosed BEFORE age 55?
  • B1Ac: Father: Did he (or any) die of prostate cancer?
    • 1=No
    • 2=Yes
    • 88=Don’t know
# B1Aa: Father: Has this person had prostate cancer?
  b1aa <- as.factor(d[,"b1aa"])
# Make "*" to NA
b1aa[which(b1aa=="*")]<-"NA"
  levels(b1aa) <- list(No="1",
                     Yes="2",
                     Dont_know="88")
  b1aa <- ordered(b1aa, c("No","Yes","Dont_know"))
  
  new.d <- data.frame(new.d, b1aa)
  new.d <- apply_labels(new.d, b1aa = "Father")
  temp.d <- data.frame (new.d, b1aa)  
  
  result<-questionr::freq(temp.d$b1aa,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1Aa: Father: Has this person had prostate cancer?")
B1Aa: Father: Has this person had prostate cancer?
n % val%
No 3207 54.9 57.3
Yes 1105 18.9 19.7
Dont_know 1283 22.0 22.9
NA 244 4.2 NA
Total 5839 100.0 100.0 
#B1Ab: Father: Was he (or any) diagnosed BEFORE age 55? 
  b1ab <- as.factor(d[,"b1ab"])
# Make "*" to NA
b1ab[which(b1ab=="*")]<-"NA"
  levels(b1ab) <- list(No="1",
                     Yes="2",
                     Dont_know="88")
  b1ab <- ordered(b1ab, c("No","Yes","Dont_know"))
  
  new.d <- data.frame(new.d, b1ab)
  new.d <- apply_labels(new.d, b1ab = "Father")
  temp.d <- data.frame (new.d, b1ab)  
  
  result<-questionr::freq(temp.d$b1ab,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1Ab: Father: Was he (or any) diagnosed BEFORE age 55?")
B1Ab: Father: Was he (or any) diagnosed BEFORE age 55?
n % val%
No 1122 19.2 58.9
Yes 130 2.2 6.8
Dont_know 652 11.2 34.2
NA 3935 67.4 NA
Total 5839 100.0 100.0 
#B1Ac: Father: Did he (or any) die of prostate cancer?
  b1ac <- as.factor(d[,"b1ac"])
  # Make "*" to NA
b1ac[which(b1ac=="*")]<-"NA"
  levels(b1ac) <- list(No="1",
                     Yes="2",
                     Dont_know="88")
  b1ac <- ordered(b1ac, c("No","Yes","Dont_know"))
  
  new.d <- data.frame(new.d, b1ac)
  new.d <- apply_labels(new.d, b1ac = "Father")
  temp.d <- data.frame (new.d, b1ac)  
  
  result<-questionr::freq(temp.d$b1ac,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1Ac: Father: Did he (or any) die of prostate cancer?")
B1Ac: Father: Did he (or any) die of prostate cancer?
n % val%
No 1235 21.2 63.6
Yes 370 6.3 19.1
Dont_know 336 5.8 17.3
NA 3898 66.8 NA
Total 5839 100.0 100.0

B1B: Any Brother

  • B1BNo: Any Brother
    • 1=I had no brothers
    • if not marked
  • B1Ba: Any Brother: Has this person had prostate cancer?
    • 1=No
    • 2=Yes
    • 88=Don’t know
  • B1Ba2: Any Brother: If Yes, number with prostate cancer
    • 1=1
    • 2=2+
  • B1Bb: Any Brother: Was he (or any) diagnosed BEFORE age 55?
    • 1=No
    • 2=Yes
    • 88=Don’t know
  • B1Bc: Any Brother: Did he (or any) die of prostate cancer?
    • 1=No
    • 2=Yes
    • 88=Don’t know
# B1BNo: Any Brother
  b1bno <- as.factor(d[,"b1bno"])
  levels(b1bno) <- list(No_brothers="1")

  new.d <- data.frame(new.d, b1bno)
  new.d <- apply_labels(new.d, b1bno = "Any Brother")
  temp.d <- data.frame (new.d, b1bno)  
  
  result<-questionr::freq(temp.d$b1bno,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1BNo: Any Brother")
B1BNo: Any Brother
n % val%
No_brothers 608 10.4 100
NA 5231 89.6 NA
Total 5839 100.0 100 
#B1Ba: Any Brother: Has this person had prostate cancer? 
  b1ba <- as.factor(d[,"b1ba"])
# Make "*" to NA
b1ba[which(b1ba=="*")]<-"NA"
  levels(b1ba) <- list(No="1",
                     Yes="2",
                     Dont_know="88")
  b1ba <- ordered(b1ba, c("No","Yes","Dont_know"))
  
  new.d <- data.frame(new.d, b1ba)
  new.d <- apply_labels(new.d, b1ba = "Any Brother: have p cancer")
  temp.d <- data.frame (new.d, b1ba)  
  
  result<-questionr::freq(temp.d$b1ba,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1Ba: Any Brother: Has this person had prostate cancer?")
B1Ba: Any Brother: Has this person had prostate cancer?
n % val%
No 3410 58.4 66.6
Yes 1152 19.7 22.5
Dont_know 560 9.6 10.9
NA 717 12.3 NA
Total 5839 100.0 100.0 
#B1Ba2: Any Brother: If Yes, number with prostate cancer
  b1ba2 <- as.factor(d[,"b1ba2"])
# Make "*" to NA
b1ba2[which(b1ba2=="*")]<-"NA"
  levels(b1ba2) <- list(One="1",
                     Two_or_more="2")
  b1ba2 <- ordered(b1ba2, c("One","Two_or_more"))
  
  new.d <- data.frame(new.d, b1ba2)
  new.d <- apply_labels(new.d, b1ba2 = "Number of brother")
  temp.d <- data.frame (new.d, b1ba2)  
  
  result<-questionr::freq(temp.d$b1ba2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1Ba2: Any Brother: If Yes, number with prostate cancer")
B1Ba2: Any Brother: If Yes, number with prostate cancer
n % val%
One 478 8.2 63.3
Two_or_more 277 4.7 36.7
NA 5084 87.1 NA
Total 5839 100.0 100.0 
#B1Bb: Any Brother: Was he (or any) diagnosed BEFORE age 55?
  b1bb <- as.factor(d[,"b1bb"])
# Make "*" to NA
b1bb[which(b1bb=="*")]<-"NA"
  levels(b1bb) <- list(No="1",
                     Yes="2",
                     Dont_know="88")
  b1bb <- ordered(b1bb, c("No","Yes","Dont_know"))
  
  new.d <- data.frame(new.d, b1bb)
  new.d <- apply_labels(new.d, b1bb = "Any Brother: before 55")
  temp.d <- data.frame (new.d, b1bb)  
  
  result<-questionr::freq(temp.d$b1bb,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1Bb: Any Brother: Was he (or any) diagnosed BEFORE age 55?")
B1Bb: Any Brother: Was he (or any) diagnosed BEFORE age 55?
n % val%
No 1108 19.0 63.2
Yes 285 4.9 16.3
Dont_know 360 6.2 20.5
NA 4086 70.0 NA
Total 5839 100.0 100.0 
#B1Bc: Any Brother: Did he (or any) die of prostate cancer?
  b1bc <- as.factor(d[,"b1bc"])
  # Make "*" to NA
b1bc[which(b1bc=="*")]<-"NA"
  levels(b1bc) <- list(No="1",
                     Yes="2",
                     Dont_know="88")
  b1bc <- ordered(b1bc, c("No","Yes","Dont_know"))
  
  new.d <- data.frame(new.d, b1bc)
  new.d <- apply_labels(new.d, b1bc = "Any Brother: die")
  temp.d <- data.frame (new.d, b1bc)  
  
  result<-questionr::freq(temp.d$b1bc,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1Bc: Any Brother: Did he (or any) die of prostate cancer?")
B1Bc: Any Brother: Did he (or any) die of prostate cancer?
n % val%
No 1417 24.3 82.0
Yes 141 2.4 8.2
Dont_know 169 2.9 9.8
NA 4112 70.4 NA
Total 5839 100.0 100.0

B1C: Any Son

  • B1CNo: Any Son
    • 1=I had no sons
    • if not marked
  • B1Ca: Any Son: Has this person had prostate cancer?
    • 1=No
    • 2=Yes
    • 88=Don’t know
  • B1Ca2: Any Son: If Yes, number with prostate cancer
    • 1=1
    • 2=2+
  • B1Cb: Any Son: Was he (or any) diagnosed BEFORE age 55?
    • 1=No
    • 2=Yes
    • 88=Don’t know
  • B1Cc: Any Son: Did he (or any) die of prostate cancer?
    • 1=No
    • 2=Yes
    • 88=Don’t know
# B1BNo
  b1cno <- as.factor(d[,"b1cno"])
  levels(b1cno) <- list(No_brothers="1")

  new.d <- data.frame(new.d, b1cno)
  new.d <- apply_labels(new.d, b1cno = "Any Son")
  temp.d <- data.frame (new.d, b1cno)  
  
  result<-questionr::freq(temp.d$b1cno,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1CNo: Any Son")
B1CNo: Any Son
n % val%
No_brothers 1082 18.5 100
NA 4757 81.5 NA
Total 5839 100.0 100 
#B1Ca
  b1ca <- as.factor(d[,"b1ca"])
  # Make "*" to NA
b1ca[which(b1ca=="*")]<-"NA"
  levels(b1ca) <- list(No="1",
                     Yes="2",
                     Dont_know="88")
  b1ca <- ordered(b1ca, c("No","Yes","Dont_know"))
  
  new.d <- data.frame(new.d, b1ca)
  new.d <- apply_labels(new.d, b1ca = "Any Son: have p cancer")
  temp.d <- data.frame (new.d, b1ca)  
  
  result<-questionr::freq(temp.d$b1ca,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1Ca: Any Son: Has this person had prostate cancer?")
B1Ca: Any Son: Has this person had prostate cancer?
n % val%
No 4244 72.7 93.1
Yes 140 2.4 3.1
Dont_know 175 3.0 3.8
NA 1280 21.9 NA
Total 5839 100.0 100.0 
#B1Ca2
  b1ca2 <- as.factor(d[,"b1ca2"])
  # Make "*" to NA
b1ca2[which(b1ca2=="*")]<-"NA"
  levels(b1ca2) <- list(One="1",
                     Two_or_more="2")
  b1ca2 <- ordered(b1ca2, c("One","Two_or_more"))
  
  new.d <- data.frame(new.d, b1ca2)
  new.d <- apply_labels(new.d, b1ca2 = "Number of sons")
  temp.d <- data.frame (new.d, b1ca2)  
  
  result<-questionr::freq(temp.d$b1ca2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1Ca2: Any Son: If Yes, number with prostate cancer")
B1Ca2: Any Son: If Yes, number with prostate cancer
n % val%
One 60 1.0 53.6
Two_or_more 52 0.9 46.4
NA 5727 98.1 NA
Total 5839 100.0 100.0 
#B1Cb
  b1cb <- as.factor(d[,"b1cb"])
  # Make "*" to NA
b1cb[which(b1cb=="*")]<-"NA"
  levels(b1cb) <- list(No="1",
                     Yes="2",
                     Dont_know="88")
  b1cb <- ordered(b1cb, c("No","Yes","Dont_know"))
  
  new.d <- data.frame(new.d, b1cb)
  new.d <- apply_labels(new.d, b1cb = "Any Son: before 55")
  temp.d <- data.frame (new.d, b1cb)  
  
  result<-questionr::freq(temp.d$b1cb,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1Cb: Any Son: Was he (or any) diagnosed BEFORE age 55?")
B1Cb: Any Son: Was he (or any) diagnosed BEFORE age 55?
n % val%
No 780 13.4 84.1
Yes 27 0.5 2.9
Dont_know 120 2.1 12.9
NA 4912 84.1 NA
Total 5839 100.0 100.0 
#B1Cc
  b1cc <- as.factor(d[,"b1cc"])
  # Make "*" to NA
b1cc[which(b1cc=="*")]<-"NA"
  levels(b1cc) <- list(No="1",
                     Yes="2",
                     Dont_know="88")
  b1cc <- ordered(b1cc, c("No","Yes","Dont_know"))
  
  new.d <- data.frame(new.d, b1cc)
  new.d <- apply_labels(new.d, b1cc = "Any Son: die")
  temp.d <- data.frame (new.d, b1cc)  
  
  result<-questionr::freq(temp.d$b1cc,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1Cc: Any Son: Did he (or any) die of prostate cancer?")
B1Cc: Any Son: Did he (or any) die of prostate cancer?
n % val%
No 817 14.0 89.2
Yes 6 0.1 0.7
Dont_know 93 1.6 10.2
NA 4923 84.3 NA
Total 5839 100.0 100.0

B1D: Maternal Grandfather

  • B1Da: Maternal Grandfather (Mom’s side): Has this person had prostate cancer?
  • B1Db: Maternal Grandfather (Mom’s side): Was he (or any) diagnosed BEFORE age 55?
  • b1Dc: Maternal Grandfather (Mom’s side): Did he (or any) die of prostate cancer?
    • 1=No
    • 2=Yes
    • 88=Don’t know
# B1Da: Maternal Grandfather (Mom’s side): Has this person had prostate cancer?
  b1da <- as.factor(d[,"b1da"])
# Make "*" to NA
b1da[which(b1da=="*")]<-"NA"
  levels(b1da) <- list(No="1",
                     Yes="2",
                     Dont_know="88")
  b1da <- ordered(b1da, c("No","Yes","Dont_know"))
  
  new.d <- data.frame(new.d, b1da)
  new.d <- apply_labels(new.d, b1da = "Father")
  temp.d <- data.frame (new.d, b1da)  
  
  result<-questionr::freq(temp.d$b1da,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1Da: Maternal Grandfather (Mom’s side): Has this person had prostate cancer?")
B1Da: Maternal Grandfather (Mom’s side): Has this person had prostate cancer?
n % val%
No 2397 41.1 43.9
Yes 174 3.0 3.2
Dont_know 2886 49.4 52.9
NA 382 6.5 NA
Total 5839 100.0 100.0 
# B1Db: Maternal Grandfather (Mom’s side): Was he (or any) diagnosed BEFORE age 55?
  b1db <- as.factor(d[,"b1db"])
  # Make "*" to NA
b1db[which(b1db=="*")]<-"NA"
  levels(b1db) <- list(No="1",
                     Yes="2",
                     Dont_know="88")
  b1db <- ordered(b1db, c("No","Yes","Dont_know"))
  
  new.d <- data.frame(new.d, b1db)
  new.d <- apply_labels(new.d, b1db = "Father")
  temp.d <- data.frame (new.d, b1db)  
  
  result<-questionr::freq(temp.d$b1db,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1Db: Maternal Grandfather (Mom’s side): Was he (or any) diagnosed BEFORE age 55?")
B1Db: Maternal Grandfather (Mom’s side): Was he (or any) diagnosed BEFORE age 55?
n % val%
No 433 7.4 36.6
Yes 21 0.4 1.8
Dont_know 730 12.5 61.7
NA 4655 79.7 NA
Total 5839 100.0 100.0 
# B1Dc: Maternal Grandfather (Mom’s  side): Did he (or any) die of prostate cancer?
  b1dc <- as.factor(d[,"b1dc"])
  # Make "*" to NA
b1dc[which(b1dc=="*")]<-"NA"
  levels(b1dc) <- list(No="1",
                     Yes="2",
                     Dont_know="88")
  b1dc <- ordered(b1dc, c("No","Yes","Dont_know"))
  
  new.d <- data.frame(new.d, b1dc)
  new.d <- apply_labels(new.d, b1dc = "Father")
  temp.d <- data.frame (new.d, b1dc)  
  
  result<-questionr::freq(temp.d$b1dc,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1Dc: Maternal Grandfather (Mom’s  side): Did he (or any) die of prostate cancer?")
B1Dc: Maternal Grandfather (Mom’s side): Did he (or any) die of prostate cancer?
n % val%
No 456 7.8 38.0
Yes 73 1.3 6.1
Dont_know 670 11.5 55.9
NA 4640 79.5 NA
Total 5839 100.0 100.0

B1E: Paternal Grandfather

  • B1Ea: Paternal Grandfather (Dad’s side): Has this person had prostate cancer?
  • B1Eb: Paternal Grandfather (Dad’s side): Was he (or any) diagnosed BEFORE age 55?
  • B1Ec: Paternal Grandfather (Dad’s side): Did he (or any) die of prostate cancer?
    • 1=No
    • 2=Yes
    • 88=Don’t know
# B1Ea: Paternal Grandfather (Dad’s side): Has this person had prostate cancer? 
  b1ea <- as.factor(d[,"b1ea"])
# Make "*" to NA
b1ea[which(b1ea=="*")]<-"NA"
  levels(b1ea) <- list(No="1",
                     Yes="2",
                     Dont_know="88")
  b1ea <- ordered(b1ea, c("No","Yes","Dont_know"))
  
  new.d <- data.frame(new.d, b1ea)
  new.d <- apply_labels(new.d, b1ea = "Father")
  temp.d <- data.frame (new.d, b1ea)  
  
  result<-questionr::freq(temp.d$b1ea,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1Ea: Paternal Grandfather (Dad’s side): Has this person had prostate cancer?")
B1Ea: Paternal Grandfather (Dad’s side): Has this person had prostate cancer?
n % val%
No 2146 36.8 39.6
Yes 166 2.8 3.1
Dont_know 3103 53.1 57.3
NA 424 7.3 NA
Total 5839 100.0 100.0 
# B1Eb: Paternal Grandfather (Dad’s side): Was he (or any) diagnosed BEFORE age 55?
  b1eb <- as.factor(d[,"b1eb"])
  # Make "*" to NA
b1eb[which(b1eb=="*")]<-"NA"
  levels(b1eb) <- list(No="1",
                     Yes="2",
                     Dont_know="88")
  b1eb <- ordered(b1eb, c("No","Yes","Dont_know"))
  
  new.d <- data.frame(new.d, b1eb)
  new.d <- apply_labels(new.d, b1eb = "Father")
  temp.d <- data.frame (new.d, b1eb)  
  
  result<-questionr::freq(temp.d$b1eb,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1Eb: Paternal Grandfather (Dad’s side): Was he (or any) diagnosed BEFORE age 55?")
B1Eb: Paternal Grandfather (Dad’s side): Was he (or any) diagnosed BEFORE age 55?
n % val%
No 372 6.4 32.3
Yes 24 0.4 2.1
Dont_know 754 12.9 65.6
NA 4689 80.3 NA
Total 5839 100.0 100.0 
# B1Ec: Paternal Grandfather (Dad’s side): Did he (or any) die of prostate cancer?
  b1ec <- as.factor(d[,"b1ec"])
  # Make "*" to NA
b1ec[which(b1ec=="*")]<-"NA"
  levels(b1ec) <- list(No="1",
                     Yes="2",
                     Dont_know="88")
  b1ec <- ordered(b1ec, c("No","Yes","Dont_know"))
  
  new.d <- data.frame(new.d, b1ec)
  new.d <- apply_labels(new.d, b1ec = "Father")
  temp.d <- data.frame (new.d, b1ec)  
  
  result<-questionr::freq(temp.d$b1ec,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1Ec: Paternal Grandfather (Dad’s side): Did he (or any) die of prostate cancer?")
B1Ec: Paternal Grandfather (Dad’s side): Did he (or any) die of prostate cancer?
n % val%
No 398 6.8 33.8
Yes 74 1.3 6.3
Dont_know 707 12.1 60.0
NA 4660 79.8 NA
Total 5839 100.0 100.0

B2: Family History (Other cancers)

  • B2. Other than prostate cancer, has any family member been diagnosed with one or more of these other cancers (only include biological or blood relatives)?
    • 2=Yes
    • 1=No
b2 <- as.factor(d[,"b2"])
# Make "*" to NA
b2[which(b2=="*")]<-"NA"
levels(b2) <- list(No="1",
                   Yes="2")
  b2 <- ordered(b2, c("Yes","No"))
  
  new.d <- data.frame(new.d, b2)
  new.d <- apply_labels(new.d, b2 = "Month Diagnosed")
  temp.d <- data.frame (new.d, b2) 
  
  result<-questionr::freq(temp.d$b2, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B2")
B2
n % val%
Yes 1455 24.9 38.7
No 2307 39.5 61.3
NA 2077 35.6 NA
Total 5839 100.0 100.0

B2A: Mother

  • B2. Other than prostate cancer, has any family member been diagnosed with one or more of these other cancers (only include biological or blood relatives)? If Yes, please indicate which family members had a cancer in the table below. Mark all that apply.
    • B2A_1: 1=Breast
    • B2A_2: 1=Ovarian
    • B2A_3: 1=Colorectal
    • B2A_4: 1=Lung
    • B2A_5: 1=Other Cancer
  b2a_1 <- as.factor(d[,"b2a_1"])
  levels(b2a_1) <- list(Breast="1")
  new.d <- data.frame(new.d, b2a_1)
  new.d <- apply_labels(new.d, b2a_1 = "Breast")
  temp.d <- data.frame (new.d, b2a_1)  
  result<-questionr::freq(temp.d$b2a_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Breast")
1. Breast
n % val%
Breast 551 9.4 100
NA 5288 90.6 NA
Total 5839 100.0 100 
  b2a_2 <- as.factor(d[,"b2a_2"])
  levels(b2a_2) <- list(Ovarian="1")
  new.d <- data.frame(new.d, b2a_2)
  new.d <- apply_labels(new.d, b2a_2 = "Ovarian")
  temp.d <- data.frame (new.d, b2a_2)  
  result<-questionr::freq(temp.d$b2a_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Ovarian")
2. Ovarian
n % val%
Ovarian 226 3.9 100
NA 5613 96.1 NA
Total 5839 100.0 100 
  b2a_3 <- as.factor(d[,"b2a_3"])
  levels(b2a_3) <- list(Colorectal="1")
  new.d <- data.frame(new.d, b2a_3)
  new.d <- apply_labels(new.d, b2a_3 = "Colorectal")
  temp.d <- data.frame (new.d, b2a_3)  
  
  result<-questionr::freq(temp.d$b2a_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Colorectal")
3. Colorectal
n % val%
Colorectal 120 2.1 100
NA 5719 97.9 NA
Total 5839 100.0 100 
  b2a_4 <- as.factor(d[,"b2a_4"])
  levels(b2a_4) <- list(Lung="1")
  new.d <- data.frame(new.d, b2a_4)
  new.d <- apply_labels(new.d, b2a_4 = "Lung")
  temp.d <- data.frame (new.d, b2a_4)  
  
  result<-questionr::freq(temp.d$b2a_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "4. Lung")
4. Lung
n % val%
Lung 176 3 100
NA 5663 97 NA
Total 5839 100 100 
  b2a_5 <- as.factor(d[,"b2a_5"])
  levels(b2a_5) <- list(Other_Cancer="1")
  new.d <- data.frame(new.d, b2a_5)
  new.d <- apply_labels(new.d, b2a_5 = "Lung")
  temp.d <- data.frame (new.d, b2a_5)  
  
  result<-questionr::freq(temp.d$b2a_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "5. Other Cancer")
5. Other Cancer
n % val%
Other_Cancer 473 8.1 100
NA 5366 91.9 NA
Total 5839 100.0 100

B2B: Father

  • B2. Other than prostate cancer, has any family member been diagnosed with one or more of these other cancers (only include biological or blood relatives)? If Yes, please indicate which family members had a cancer in the table below. Mark all that apply.
    • B2B_1: 1=Breast
    • B2B_3: 1=Colorectal
    • B2B_4: 1=Lung
    • B2B_5: 1=Other Cancer
  b2b_1 <- as.factor(d[,"b2b_1"])
  levels(b2b_1) <- list(Breast="1")
  new.d <- data.frame(new.d, b2b_1)
  new.d <- apply_labels(new.d, b2b_1 = "Breast")
  temp.d <- data.frame (new.d, b2b_1)  
  result<-questionr::freq(temp.d$b2b_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Breast")
1. Breast
n % val%
Breast 39 0.7 100
NA 5800 99.3 NA
Total 5839 100.0 100 
  b2b_3 <- as.factor(d[,"b2b_3"])
  levels(b2b_3) <- list(Colorectal="1")
  new.d <- data.frame(new.d, b2b_3)
  new.d <- apply_labels(new.d, b2b_3 = "Colorectal")
  temp.d <- data.frame (new.d, b2b_3)  
  
  result<-questionr::freq(temp.d$b2b_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Colorectal")
3. Colorectal
n % val%
Colorectal 123 2.1 100
NA 5716 97.9 NA
Total 5839 100.0 100 
  b2b_4 <- as.factor(d[,"b2b_4"])
  levels(b2b_4) <- list(Lung="1")
  new.d <- data.frame(new.d, b2b_4)
  new.d <- apply_labels(new.d, b2b_4 = "Lung")
  temp.d <- data.frame (new.d, b2b_4)  
  
  result<-questionr::freq(temp.d$b2b_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "4. Lung")
4. Lung
n % val%
Lung 304 5.2 100
NA 5535 94.8 NA
Total 5839 100.0 100 
  b2b_5 <- as.factor(d[,"b2b_5"])
  levels(b2b_5) <- list(Other_Cancer="1")
  new.d <- data.frame(new.d, b2b_5)
  new.d <- apply_labels(new.d, b2b_5 = "Lung")
  temp.d <- data.frame (new.d, b2b_5)  
  
  result<-questionr::freq(temp.d$b2b_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "5. Other Cancer")
5. Other Cancer
n % val%
Other_Cancer 416 7.1 100
NA 5423 92.9 NA
Total 5839 100.0 100

B2C: Any sister

  • B2. Other than prostate cancer, has any family member been diagnosed with one or more of these other cancers (only include biological or blood relatives)? If Yes, please indicate which family members had a cancer in the table below. Mark all that apply.
    • B2C_1: 1=Breast
    • B2C_2: 1=Ovarian
    • B2C_3: 1=Colorectal
    • B2C_4: 1=Lung
    • B2C_5: 1=Other Cancer
  b2c_1 <- as.factor(d[,"b2c_1"])
  levels(b2c_1) <- list(Breast="1")
  new.d <- data.frame(new.d, b2c_1)
  new.d <- apply_labels(new.d, b2c_1 = "Breast")
  temp.d <- data.frame (new.d, b2c_1)  
  result<-questionr::freq(temp.d$b2c_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Breast")
1. Breast
n % val%
Breast 593 10.2 100
NA 5246 89.8 NA
Total 5839 100.0 100 
  b2c_2 <- as.factor(d[,"b2c_2"])
  levels(b2c_2) <- list(Ovarian="1")
  new.d <- data.frame(new.d, b2c_2)
  new.d <- apply_labels(new.d, b2c_2 = "Ovarian")
  temp.d <- data.frame (new.d, b2c_2)  
  result<-questionr::freq(temp.d$b2c_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Ovarian")
2. Ovarian
n % val%
Ovarian 171 2.9 100
NA 5668 97.1 NA
Total 5839 100.0 100 
  b2c_3 <- as.factor(d[,"b2c_3"])
  levels(b2c_3) <- list(Colorectal="1")
  new.d <- data.frame(new.d, b2c_3)
  new.d <- apply_labels(new.d, b2c_3 = "Colorectal")
  temp.d <- data.frame (new.d, b2c_3)  
  
  result<-questionr::freq(temp.d$b2c_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Colorectal")
3. Colorectal
n % val%
Colorectal 61 1 100
NA 5778 99 NA
Total 5839 100 100 
  b2c_4 <- as.factor(d[,"b2c_4"])
  levels(b2c_4) <- list(Lung="1")
  new.d <- data.frame(new.d, b2c_4)
  new.d <- apply_labels(new.d, b2c_4 = "Lung")
  temp.d <- data.frame (new.d, b2c_4)  
  
  result<-questionr::freq(temp.d$b2c_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "4. Lung")
4. Lung
n % val%
Lung 130 2.2 100
NA 5709 97.8 NA
Total 5839 100.0 100 
  b2c_5 <- as.factor(d[,"b2c_5"])
  levels(b2c_5) <- list(Other_Cancer="1")
  new.d <- data.frame(new.d, b2c_5)
  new.d <- apply_labels(new.d, b2c_5 = "Lung")
  temp.d <- data.frame (new.d, b2c_5)  
  
  result<-questionr::freq(temp.d$b2c_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "5. Other Cancer")
5. Other Cancer
n % val%
Other_Cancer 311 5.3 100
NA 5528 94.7 NA
Total 5839 100.0 100

B2D: Any brother

  • B2. Other than prostate cancer, has any family member been diagnosed with one or more of these other cancers (only include biological or blood relatives)? If Yes, please indicate which family members had a cancer in the table below. Mark all that apply.
    • B2D_1: 1=Breast
    • B2D_3: 1=Colorectal
    • B2D_4: 1=Lung
    • B2D_5: 1=Other Cancer
  b2d_1 <- as.factor(d[,"b2d_1"])
  levels(b2d_1) <- list(Breast="1")
  new.d <- data.frame(new.d, b2d_1)
  new.d <- apply_labels(new.d, b2d_1 = "Breast")
  temp.d <- data.frame (new.d, b2d_1)  
  result<-questionr::freq(temp.d$b2d_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Breast")
1. Breast
n % val%
Breast 39 0.7 100
NA 5800 99.3 NA
Total 5839 100.0 100 
  b2d_3 <- as.factor(d[,"b2d_3"])
  levels(b2d_3) <- list(Colorectal="1")
  new.d <- data.frame(new.d, b2d_3)
  new.d <- apply_labels(new.d, b2d_3 = "Colorectal")
  temp.d <- data.frame (new.d, b2d_3)  
  
  result<-questionr::freq(temp.d$b2d_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Colorectal")
3. Colorectal
n % val%
Colorectal 88 1.5 100
NA 5751 98.5 NA
Total 5839 100.0 100 
  b2d_4 <- as.factor(d[,"b2d_4"])
  levels(b2d_4) <- list(Lung="1")
  new.d <- data.frame(new.d, b2d_4)
  new.d <- apply_labels(new.d, b2d_4 = "Lung")
  temp.d <- data.frame (new.d, b2d_4)  
  
  result<-questionr::freq(temp.d$b2d_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "4. Lung")
4. Lung
n % val%
Lung 169 2.9 100
NA 5670 97.1 NA
Total 5839 100.0 100 
  b2d_5 <- as.factor(d[,"b2d_5"])
  levels(b2d_5) <- list(Other_Cancer="1")
  new.d <- data.frame(new.d, b2d_5)
  new.d <- apply_labels(new.d, b2d_5 = "Lung")
  temp.d <- data.frame (new.d, b2d_5)  
  
  result<-questionr::freq(temp.d$b2d_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "5. Other Cancer")
5. Other Cancer
n % val%
Other_Cancer 375 6.4 100
NA 5464 93.6 NA
Total 5839 100.0 100

B2E: Any daughter

  • B2. Other than prostate cancer, has any family member been diagnosed with one or more of these other cancers (only include biological or blood relatives)? If Yes, please indicate which family members had a cancer in the table below. Mark all that apply.
    • B2E_1: 1=Breast
    • B2E_2: 1=Ovarian
    • B2E_3: 1=Colorectal
    • B2E_4: 1=Lung
    • B2E_5: 1=Other Cancer
  b2e_1 <- as.factor(d[,"b2e_1"])
  levels(b2e_1) <- list(Breast="1")
  new.d <- data.frame(new.d, b2e_1)
  new.d <- apply_labels(new.d, b2e_1 = "Breast")
  temp.d <- data.frame (new.d, b2e_1)  
  result<-questionr::freq(temp.d$b2e_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Breast")
1. Breast
n % val%
Breast 55 0.9 100
NA 5784 99.1 NA
Total 5839 100.0 100 
  b2e_2 <- as.factor(d[,"b2e_2"])
  levels(b2e_2) <- list(Ovarian="1")
  new.d <- data.frame(new.d, b2e_2)
  new.d <- apply_labels(new.d, b2e_2 = "Ovarian")
  temp.d <- data.frame (new.d, b2e_2)  
  result<-questionr::freq(temp.d$b2e_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Ovarian")
2. Ovarian
n % val%
Ovarian 89 1.5 100
NA 5750 98.5 NA
Total 5839 100.0 100 
  b2e_3 <- as.factor(d[,"b2e_3"])
  levels(b2e_3) <- list(Colorectal="1")
  new.d <- data.frame(new.d, b2e_3)
  new.d <- apply_labels(new.d, b2e_3 = "Colorectal")
  temp.d <- data.frame (new.d, b2e_3)  
  
  result<-questionr::freq(temp.d$b2e_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Colorectal")
3. Colorectal
n % val%
Colorectal 5 0.1 100
NA 5834 99.9 NA
Total 5839 100.0 100 
  b2e_4 <- as.factor(d[,"b2e_4"])
  levels(b2e_4) <- list(Lung="1")
  new.d <- data.frame(new.d, b2e_4)
  new.d <- apply_labels(new.d, b2e_4 = "Lung")
  temp.d <- data.frame (new.d, b2e_4)  
  
  result<-questionr::freq(temp.d$b2e_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "4. Lung")
4. Lung
n % val%
Lung 9 0.2 100
NA 5830 99.8 NA
Total 5839 100.0 100 
  b2e_5 <- as.factor(d[,"b2e_5"])
  levels(b2e_5) <- list(Other_Cancer="1")
  new.d <- data.frame(new.d, b2e_5)
  new.d <- apply_labels(new.d, b2e_5 = "Lung")
  temp.d <- data.frame (new.d, b2e_5)  
  
  result<-questionr::freq(temp.d$b2e_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "5. Other Cancer")
5. Other Cancer
n % val%
Other_Cancer 29 0.5 100
NA 5810 99.5 NA
Total 5839 100.0 100

B2F: Any son

  • B2. Other than prostate cancer, has any family member been diagnosed with one or more of these other cancers (only include biological or blood relatives)? If Yes, please indicate which family members had a cancer in the table below. Mark all that apply.
    • B2F_1: 1=Breast
    • B2F_3: 1=Colorectal
    • B2F_4: 1=Lung
    • B2F_5: 1=Other Cancer
  b2f_1 <- as.factor(d[,"b2f_1"])
  levels(b2f_1) <- list(Breast="1")
  new.d <- data.frame(new.d, b2f_1)
  new.d <- apply_labels(new.d, b2f_1 = "Breast")
  temp.d <- data.frame (new.d, b2f_1)  
  result<-questionr::freq(temp.d$b2f_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Breast")
1. Breast
n % val%
Breast 16 0.3 100
NA 5823 99.7 NA
Total 5839 100.0 100 
  b2f_3 <- as.factor(d[,"b2f_3"])
  levels(b2f_3) <- list(Colorectal="1")
  new.d <- data.frame(new.d, b2f_3)
  new.d <- apply_labels(new.d, b2f_3 = "Colorectal")
  temp.d <- data.frame (new.d, b2f_3)  
  
  result<-questionr::freq(temp.d$b2f_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Colorectal")
3. Colorectal
n % val%
Colorectal 12 0.2 100
NA 5827 99.8 NA
Total 5839 100.0 100 
  b2f_4 <- as.factor(d[,"b2f_4"])
  levels(b2f_4) <- list(Lung="1")
  new.d <- data.frame(new.d, b2f_4)
  new.d <- apply_labels(new.d, b2f_4 = "Lung")
  temp.d <- data.frame (new.d, b2f_4)  
  
  result<-questionr::freq(temp.d$b2f_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "4. Lung")
4. Lung
n % val%
Lung 8 0.1 100
NA 5831 99.9 NA
Total 5839 100.0 100 
  b2f_5 <- as.factor(d[,"b2f_5"])
  levels(b2f_5) <- list(Other_Cancer="1")
  new.d <- data.frame(new.d, b2f_5)
  new.d <- apply_labels(new.d, b2f_5 = "Lung")
  temp.d <- data.frame (new.d, b2f_5)  
  
  result<-questionr::freq(temp.d$b2f_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "5. Other Cancer")
5. Other Cancer
n % val%
Other_Cancer 28 0.5 100
NA 5811 99.5 NA
Total 5839 100.0 100

B3: Current health

  • B3. In general, how would you rate your current health?
    • 1=Excellent
    • 2=Very Good
    • 3=Good
    • 4=Fair
    • 5=Poor
  b3 <- as.factor(d[,"b3"])
# Make "*" to NA
b3[which(b3=="*")]<-"NA"
  levels(b3) <- list(Excellent="1",
                     Very_Good="2",
                     Good="3",
                     Fair="4",
                     Poor="5")
  b3 <- ordered(b3, c("Excellent","Very_Good","Good","Fair","Poor"))

  new.d <- data.frame(new.d, b3)
  new.d <- apply_labels(new.d, b3 = "Current Health")
  temp.d <- data.frame (new.d, b3)  
  
  result<-questionr::freq(temp.d$b3, cum = TRUE, total = TRUE)
  kable(result, format = "simple", align = 'l')
n % val% %cum val%cum
Excellent 387 6.6 6.9 6.6 6.9
Very_Good 1566 26.8 27.9 33.4 34.8
Good 2378 40.7 42.3 74.2 77.1
Fair 1149 19.7 20.5 93.9 97.5
Poor 138 2.4 2.5 96.2 100.0
NA 221 3.8 NA 100.0 NA
Total 5839 100.0 100.0 100.0 100.0

B4: Comorbidities

  • B4. Has the doctor ever told you that you have/had…
    • Heart Attack
    • Heart Failure or CHF
    • Stroke
    • Hypertension
    • Peripheral arterial disease
    • High Cholesterol
    • Asthma, COPD
    • Stomach ulcers
    • Crohn’s Disease
    • Diabetes
    • Kidney Problems
    • Cirrhosis, liver damage
    • Arthritis
    • Dementia
    • Depression
    • AIDS
    • Other Cancer
# Heart Attack
  b4aa <- as.factor(d[,"b4aa"])
# Make "*" to NA
b4aa[which(b4aa=="*")]<-"NA"
  levels(b4aa) <- list(No="1",
                     Yes="2")
  b4aa <- ordered(b4aa, c("No", "Yes"))
  
  new.d <- data.frame(new.d, b4aa)
  new.d <- apply_labels(new.d, b4aa = "Heart Attack")
  temp.d <- data.frame (new.d, b4aa)  
  
  result<-questionr::freq(temp.d$b4aa, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Heart Attack")
Heart Attack
n % val%
No 5145 88.1 93.1
Yes 379 6.5 6.9
NA 315 5.4 NA
Total 5839 100.0 100.0 
  b4ab <- as.factor(d[,"b4ab"])
  new.d <- data.frame(new.d, b4ab)
  new.d <- apply_labels(new.d, b4ab = "Heart Attack age")
  temp.d <- data.frame (new.d, b4ab)  
  result<-questionr::freq(temp.d$b4ab, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Heart Attack Age")
Heart Attack Age
n % val%
0 77 1.3 17.9
1 2 0.0 0.5
14 1 0.0 0.2
17 3 0.1 0.7
19 1 0.0 0.2
20 1 0.0 0.2
23 2 0.0 0.5
24 2 0.0 0.5
25 2 0.0 0.5
26 1 0.0 0.2
27 2 0.0 0.5
29 4 0.1 0.9
31 2 0.0 0.5
32 2 0.0 0.5
33 1 0.0 0.2
34 1 0.0 0.2
35 4 0.1 0.9
36 1 0.0 0.2
38 3 0.1 0.7
40 7 0.1 1.6
41 2 0.0 0.5
42 1 0.0 0.2
44 3 0.1 0.7
45 8 0.1 1.9
46 6 0.1 1.4
47 3 0.1 0.7
48 8 0.1 1.9
49 6 0.1 1.4
5 1 0.0 0.2
50 26 0.4 6.1
51 9 0.2 2.1
52 14 0.2 3.3
53 8 0.1 1.9
54 9 0.2 2.1
55 19 0.3 4.4
56 5 0.1 1.2
57 13 0.2 3.0
58 20 0.3 4.7
59 12 0.2 2.8
60 21 0.4 4.9
61 10 0.2 2.3
62 6 0.1 1.4
63 9 0.2 2.1
64 15 0.3 3.5
65 11 0.2 2.6
66 5 0.1 1.2
67 9 0.2 2.1
68 4 0.1 0.9
69 11 0.2 2.6
70 8 0.1 1.9
71 7 0.1 1.6
72 4 0.1 0.9
73 4 0.1 0.9
74 7 0.1 1.6
75 1 0.0 0.2
76 3 0.1 0.7
79 1 0.0 0.2
93 1 0.0 0.2
NA 5410 92.7 NA
Total 5839 100.0 100.0 
# Heart Failure or CHF
  b4ba <- as.factor(d[,"b4ba"])
  # Make "*" to NA
b4ba[which(b4ba=="*")]<-"NA"
  levels(b4ba) <- list(No="1",
                     Yes="2")
  b4ba <- ordered(b4ba, c("No", "Yes"))
  
  new.d <- data.frame(new.d, b4ba)
  new.d <- apply_labels(new.d, b4ba = "Heart Failure or CHF")
  temp.d <- data.frame (new.d, b4ba)  
  
  result<-questionr::freq(temp.d$b4ba, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Heart Failure or CHF")
Heart Failure or CHF
n % val%
No 5134 87.9 93.9
Yes 333 5.7 6.1
NA 372 6.4 NA
Total 5839 100.0 100.0 
  b4bb <- as.factor(d[,"b4bb"])
  new.d <- data.frame(new.d, b4bb)
  new.d <- apply_labels(new.d, b4bb = "Heart Failure or CHF age")
  temp.d <- data.frame (new.d, b4bb)  
  result<-questionr::freq(temp.d$b4bb, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Heart Failure or CHF Age")
Heart Failure or CHF Age
n % val%
1 1 0.0 0.3
12 1 0.0 0.3
13 1 0.0 0.3
17 1 0.0 0.3
18 1 0.0 0.3
20 2 0.0 0.7
24 1 0.0 0.3
25 1 0.0 0.3
27 1 0.0 0.3
28 1 0.0 0.3
29 1 0.0 0.3
30 2 0.0 0.7
31 1 0.0 0.3
32 1 0.0 0.3
34 1 0.0 0.3
35 2 0.0 0.7
36 1 0.0 0.3
39 2 0.0 0.7
4 1 0.0 0.3
40 5 0.1 1.7
42 1 0.0 0.3
43 3 0.1 1.0
45 2 0.0 0.7
46 3 0.1 1.0
47 4 0.1 1.3
48 4 0.1 1.3
49 7 0.1 2.3
5 2 0.0 0.7
50 15 0.3 5.0
51 5 0.1 1.7
52 11 0.2 3.7
53 5 0.1 1.7
54 6 0.1 2.0
55 10 0.2 3.3
56 12 0.2 4.0
57 6 0.1 2.0
58 9 0.2 3.0
59 12 0.2 4.0
60 20 0.3 6.7
61 7 0.1 2.3
62 15 0.3 5.0
63 12 0.2 4.0
64 9 0.2 3.0
65 13 0.2 4.3
66 7 0.1 2.3
67 7 0.1 2.3
68 11 0.2 3.7
69 7 0.1 2.3
7 1 0.0 0.3
70 10 0.2 3.3
71 7 0.1 2.3
72 7 0.1 2.3
73 5 0.1 1.7
74 3 0.1 1.0
75 4 0.1 1.3
76 1 0.0 0.3
77 1 0.0 0.3
78 2 0.0 0.7
80 2 0.0 0.7
82 1 0.0 0.3
83 1 0.0 0.3
95 1 0.0 0.3
97 1 0.0 0.3
NA 5539 94.9 NA
Total 5839 100.0 100.0 
# Stroke  
  b4ca <- as.factor(d[,"b4ca"])
  # Make "*" to NA
b4ca[which(b4ca=="*")]<-"NA"
  levels(b4ca) <- list(No="1",
                     Yes="2")
  b4ca <- ordered(b4ca, c("No", "Yes"))
  
  new.d <- data.frame(new.d, b4ca)
  new.d <- apply_labels(new.d, b4ca = "Stroke")
  temp.d <- data.frame (new.d, b4ca)  
  
  result<-questionr::freq(temp.d$b4ca,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Stroke")
Stroke
n % val%
No 4989 85.4 91.1
Yes 486 8.3 8.9
NA 364 6.2 NA
Total 5839 100.0 100.0 
  b4cb <- as.factor(d[,"b4cb"])
  new.d <- data.frame(new.d, b4cb)
  new.d <- apply_labels(new.d, b4cb = "Stroke age")
  temp.d <- data.frame (new.d, b4cb)  
  result<-questionr::freq(temp.d$b4cb, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Stroke Age")
Stroke Age
n % val%
0 104 1.8 19.0
12 1 0.0 0.2
15 1 0.0 0.2
16 1 0.0 0.2
19 1 0.0 0.2
20 2 0.0 0.4
23 1 0.0 0.2
25 4 0.1 0.7
27 3 0.1 0.5
30 3 0.1 0.5
31 2 0.0 0.4
34 2 0.0 0.4
35 3 0.1 0.5
38 1 0.0 0.2
40 6 0.1 1.1
42 5 0.1 0.9
43 5 0.1 0.9
45 11 0.2 2.0
46 6 0.1 1.1
47 4 0.1 0.7
48 7 0.1 1.3
49 6 0.1 1.1
5 3 0.1 0.5
50 19 0.3 3.5
51 8 0.1 1.5
52 14 0.2 2.6
53 4 0.1 0.7
54 14 0.2 2.6
55 21 0.4 3.8
56 15 0.3 2.7
57 16 0.3 2.9
58 10 0.2 1.8
59 18 0.3 3.3
60 27 0.5 4.9
61 17 0.3 3.1
62 15 0.3 2.7
63 23 0.4 4.2
64 16 0.3 2.9
65 23 0.4 4.2
66 13 0.2 2.4
67 10 0.2 1.8
68 23 0.4 4.2
69 15 0.3 2.7
70 9 0.2 1.6
71 4 0.1 0.7
72 5 0.1 0.9
73 3 0.1 0.5
74 3 0.1 0.5
75 2 0.0 0.4
76 3 0.1 0.5
77 3 0.1 0.5
78 3 0.1 0.5
79 4 0.1 0.7
8 1 0.0 0.2
80 1 0.0 0.2
86 1 0.0 0.2
88 1 0.0 0.2
9 1 0.0 0.2
NA 5292 90.6 NA
Total 5839 100.0 100.0 
# Hypertension 
  b4da <- as.factor(d[,"b4da"])
# Make "*" to NA
b4da[which(b4da=="*")]<-"NA"
  levels(b4da) <- list(No="1",
                     Yes="2")
  b4da <- ordered(b4da, c("No", "Yes"))
  
  new.d <- data.frame(new.d, b4da)
  new.d <- apply_labels(new.d, b4da = "Hypertension")
  temp.d <- data.frame (new.d, b4da)  
  
  result<-questionr::freq(temp.d$b4da, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Hypertension")
Hypertension
n % val%
No 1500 25.7 26.8
Yes 4089 70.0 73.2
NA 250 4.3 NA
Total 5839 100.0 100.0 
  b4db <- as.factor(d[,"b4db"])
  new.d <- data.frame(new.d, b4db)
  new.d <- apply_labels(new.d, b4db = "Hypertension age")
  temp.d <- data.frame (new.d, b4db)  
  result<-questionr::freq(temp.d$b4db, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Hypertension Age")
Hypertension Age
n % val%
.35 1 0.0 0.0
1 2 0.0 0.1
10 1 0.0 0.0
12 5 0.1 0.1
13 2 0.0 0.1
14 2 0.0 0.1
15 5 0.1 0.1
16 11 0.2 0.3
17 3 0.1 0.1
18 12 0.2 0.3
19 6 0.1 0.2
2 3 0.1 0.1
20 19 0.3 0.5
21 6 0.1 0.2
22 12 0.2 0.3
23 11 0.2 0.3
24 13 0.2 0.4
25 32 0.5 0.9
26 6 0.1 0.2
27 12 0.2 0.3
28 18 0.3 0.5
29 14 0.2 0.4
30 109 1.9 3.0
31 19 0.3 0.5
32 26 0.4 0.7
33 16 0.3 0.4
34 15 0.3 0.4
35 144 2.5 4.0
36 27 0.5 0.8
37 20 0.3 0.6
38 42 0.7 1.2
39 31 0.5 0.9
4 3 0.1 0.1
40 270 4.6 7.5
41 26 0.4 0.7
42 60 1.0 1.7
43 28 0.5 0.8
44 31 0.5 0.9
45 250 4.3 7.0
46 41 0.7 1.1
47 45 0.8 1.3
48 60 1.0 1.7
49 46 0.8 1.3
5 5 0.1 0.1
50 409 7.0 11.4
51 45 0.8 1.3
52 78 1.3 2.2
53 37 0.6 1.0
54 63 1.1 1.8
55 271 4.6 7.5
56 76 1.3 2.1
57 66 1.1 1.8
58 99 1.7 2.8
59 54 0.9 1.5
6 1 0.0 0.0
60 272 4.7 7.6
61 55 0.9 1.5
62 87 1.5 2.4
63 47 0.8 1.3
64 50 0.9 1.4
65 92 1.6 2.6
66 23 0.4 0.6
67 38 0.7 1.1
68 45 0.8 1.3
69 26 0.4 0.7
7 2 0.0 0.1
70 47 0.8 1.3
71 17 0.3 0.5
72 18 0.3 0.5
73 10 0.2 0.3
74 8 0.1 0.2
75 5 0.1 0.1
76 4 0.1 0.1
77 1 0.0 0.0
78 1 0.0 0.0
79 4 0.1 0.1
8 4 0.1 0.1
80 3 0.1 0.1
81 1 0.0 0.0
89 3 0.1 0.1
9 4 0.1 0.1
91 1 0.0 0.0
92 1 0.0 0.0
93 1 0.0 0.0
94 3 0.1 0.1
96 1 0.0 0.0
97 1 0.0 0.0
98 1 0.0 0.0
99 5 0.1 0.1
NA 2249 38.5 NA
Total 5839 100.0 100.0 
# Peripheral arterial disease 
  b4ea <- as.factor(d[,"b4ea"])
# Make "*" to NA
b4ea[which(b4ea=="*")]<-"NA"  
  levels(b4ea) <- list(No="1",
                     Yes="2")
  b4ea <- ordered(b4ea, c("No", "Yes"))
  
  new.d <- data.frame(new.d, b4ea)
  new.d <- apply_labels(new.d, b4ea = "Peripheral arterial disease")
  temp.d <- data.frame (new.d, b4ea)  
  
  result<-questionr::freq(temp.d$b4ea,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Peripheral arterial disease")
Peripheral arterial disease
n % val%
No 5048 86.5 94
Yes 324 5.5 6
NA 467 8.0 NA
Total 5839 100.0 100 
  b4eb <- as.factor(d[,"b4eb"])
  new.d <- data.frame(new.d, b4eb)
  new.d <- apply_labels(new.d, b4eb = "Peripheral arterial disease age")
  temp.d <- data.frame (new.d, b4eb)  
  result<-questionr::freq(temp.d$b4eb, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Peripheral arterial disease Age")
Peripheral arterial disease Age
n % val%
* 1 0.0 0.3
** 1 0.0 0.3
0 27 0.5 6.8
1 1 0.0 0.3
16 1 0.0 0.3
17 1 0.0 0.3
19 1 0.0 0.3
20 1 0.0 0.3
22 1 0.0 0.3
25 3 0.1 0.8
26 1 0.0 0.3
30 2 0.0 0.5
31 1 0.0 0.3
33 1 0.0 0.3
34 3 0.1 0.8
35 5 0.1 1.3
36 1 0.0 0.3
37 1 0.0 0.3
40 16 0.3 4.1
41 1 0.0 0.3
42 2 0.0 0.5
43 1 0.0 0.3
44 2 0.0 0.5
45 8 0.1 2.0
46 2 0.0 0.5
47 2 0.0 0.5
48 8 0.1 2.0
49 4 0.1 1.0
5 1 0.0 0.3
50 36 0.6 9.1
51 7 0.1 1.8
52 4 0.1 1.0
53 4 0.1 1.0
54 8 0.1 2.0
55 23 0.4 5.8
56 8 0.1 2.0
57 11 0.2 2.8
58 16 0.3 4.1
59 7 0.1 1.8
60 32 0.5 8.1
61 6 0.1 1.5
62 14 0.2 3.5
63 8 0.1 2.0
64 13 0.2 3.3
65 22 0.4 5.6
66 6 0.1 1.5
67 7 0.1 1.8
68 12 0.2 3.0
69 8 0.1 2.0
70 17 0.3 4.3
71 2 0.0 0.5
72 4 0.1 1.0
73 5 0.1 1.3
74 3 0.1 0.8
76 3 0.1 0.8
77 1 0.0 0.3
78 2 0.0 0.5
82 2 0.0 0.5
84 1 0.0 0.3
94 1 0.0 0.3
97 1 0.0 0.3
98 1 0.0 0.3
NA 5444 93.2 NA
Total 5839 100.0 100.0 
# High Cholesterol 
  b4fa <- as.factor(d[,"b4fa"])
  # Make "*" to NA
b4fa[which(b4fa=="*")]<-"NA"
  levels(b4fa) <- list(No="1",
                     Yes="2")
  b4fa <- ordered(b4fa, c("No", "Yes"))
  
  new.d <- data.frame(new.d, b4fa)
  new.d <- apply_labels(new.d, b4fa = "High Cholesterol")
  temp.d <- data.frame (new.d, b4fa)  
  
  result<-questionr::freq(temp.d$b4fa, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "High Cholesterol")
High Cholesterol
n % val%
No 2499 42.8 45.4
Yes 3004 51.4 54.6
NA 336 5.8 NA
Total 5839 100.0 100.0 
  b4fb <- as.factor(d[,"b4fb"])
  new.d <- data.frame(new.d, b4fb)
  new.d <- apply_labels(new.d, b4fb = "High Cholesterol age")
  temp.d <- data.frame (new.d, b4fb)  
  result<-questionr::freq(temp.d$b4fb, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "High Cholesterol Age")
High Cholesterol Age
n % val%
1 3 0.1 0.1
10 5 0.1 0.2
12 2 0.0 0.1
13 1 0.0 0.0
14 1 0.0 0.0
15 3 0.1 0.1
16 2 0.0 0.1
17 1 0.0 0.0
18 1 0.0 0.0
19 4 0.1 0.2
2 2 0.0 0.1
20 5 0.1 0.2
21 4 0.1 0.2
22 3 0.1 0.1
23 1 0.0 0.0
24 8 0.1 0.3
25 7 0.1 0.3
26 4 0.1 0.2
27 4 0.1 0.2
28 8 0.1 0.3
29 5 0.1 0.2
30 34 0.6 1.4
31 4 0.1 0.2
32 12 0.2 0.5
33 6 0.1 0.2
34 12 0.2 0.5
35 53 0.9 2.1
36 20 0.3 0.8
37 6 0.1 0.2
38 23 0.4 0.9
39 12 0.2 0.5
4 1 0.0 0.0
40 134 2.3 5.4
41 17 0.3 0.7
42 27 0.5 1.1
43 15 0.3 0.6
44 16 0.3 0.6
45 148 2.5 5.9
46 30 0.5 1.2
47 19 0.3 0.8
48 48 0.8 1.9
49 28 0.5 1.1
5 6 0.1 0.2
50 295 5.1 11.9
51 35 0.6 1.4
52 54 0.9 2.2
53 31 0.5 1.2
54 47 0.8 1.9
55 227 3.9 9.1
56 55 0.9 2.2
57 66 1.1 2.7
58 79 1.4 3.2
59 49 0.8 2.0
6 2 0.0 0.1
60 221 3.8 8.9
61 35 0.6 1.4
62 85 1.5 3.4
63 44 0.8 1.8
64 44 0.8 1.8
65 93 1.6 3.7
66 25 0.4 1.0
67 30 0.5 1.2
68 43 0.7 1.7
69 30 0.5 1.2
7 2 0.0 0.1
70 60 1.0 2.4
71 13 0.2 0.5
72 17 0.3 0.7
73 12 0.2 0.5
74 12 0.2 0.5
75 14 0.2 0.6
76 6 0.1 0.2
77 3 0.1 0.1
8 1 0.0 0.0
80 1 0.0 0.0
86 1 0.0 0.0
87 1 0.0 0.0
9 1 0.0 0.0
90 1 0.0 0.0
91 2 0.0 0.1
92 1 0.0 0.0
94 1 0.0 0.0
95 1 0.0 0.0
96 1 0.0 0.0
97 1 0.0 0.0
99 2 0.0 0.1
NA 3350 57.4 NA
Total 5839 100.0 100.0 
#  Asthma, COPD
  b4ga <- as.factor(d[,"b4ga"])
  # Make "*" to NA
b4ga[which(b4ga=="*")]<-"NA"
  levels(b4ga) <- list(No="1",
                     Yes="2")
  b4ga <- ordered(b4ga, c("No", "Yes"))
  
  new.d <- data.frame(new.d, b4ga)
  new.d <- apply_labels(new.d, b4ga = "Asthma, COPD")
  temp.d <- data.frame (new.d, b4ga)  
  
  result<-questionr::freq(temp.d$b4ga, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Asthma, COPD")
Asthma, COPD
n % val%
No 4826 82.7 85
Yes 853 14.6 15
NA 160 2.7 NA
Total 5839 100.0 100 
  b4gb <- as.factor(d[,"b4gb"])
  new.d <- data.frame(new.d, b4gb)
  new.d <- apply_labels(new.d, b4gb = "Asthma, COPD age")
  temp.d <- data.frame (new.d, b4gb)  
  result<-questionr::freq(temp.d$b4gb, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Asthma, COPD Age")
Asthma, COPD Age
n % val%
04 1 0.0 0.1
06 2 0.0 0.3
1 16 0.3 2.1
10 37 0.6 4.8
11 6 0.1 0.8
12 17 0.3 2.2
13 5 0.1 0.7
14 5 0.1 0.7
15 6 0.1 0.8
16 6 0.1 0.8
17 2 0.0 0.3
18 14 0.2 1.8
19 3 0.1 0.4
2 10 0.2 1.3
20 8 0.1 1.0
21 2 0.0 0.3
22 2 0.0 0.3
23 1 0.0 0.1
24 3 0.1 0.4
25 6 0.1 0.8
26 2 0.0 0.3
27 3 0.1 0.4
28 3 0.1 0.4
29 1 0.0 0.1
3 5 0.1 0.7
30 13 0.2 1.7
31 2 0.0 0.3
32 3 0.1 0.4
33 2 0.0 0.3
35 9 0.2 1.2
36 2 0.0 0.3
37 1 0.0 0.1
38 6 0.1 0.8
39 1 0.0 0.1
4 12 0.2 1.6
40 19 0.3 2.5
42 6 0.1 0.8
43 3 0.1 0.4
44 2 0.0 0.3
45 14 0.2 1.8
46 4 0.1 0.5
47 4 0.1 0.5
48 6 0.1 0.8
49 5 0.1 0.7
5 47 0.8 6.1
50 41 0.7 5.4
51 7 0.1 0.9
52 7 0.1 0.9
53 5 0.1 0.7
54 5 0.1 0.7
55 24 0.4 3.1
56 15 0.3 2.0
57 15 0.3 2.0
58 12 0.2 1.6
59 18 0.3 2.3
6 19 0.3 2.5
60 50 0.9 6.5
61 7 0.1 0.9
62 19 0.3 2.5
63 19 0.3 2.5
64 11 0.2 1.4
65 29 0.5 3.8
66 8 0.1 1.0
67 9 0.2 1.2
68 10 0.2 1.3
69 7 0.1 0.9
7 15 0.3 2.0
70 19 0.3 2.5
71 6 0.1 0.8
72 5 0.1 0.7
73 8 0.1 1.0
74 4 0.1 0.5
75 5 0.1 0.7
76 3 0.1 0.4
77 5 0.1 0.7
78 2 0.0 0.3
79 1 0.0 0.1
8 9 0.2 1.2
80 2 0.0 0.3
81 2 0.0 0.3
9 13 0.2 1.7
93 1 0.0 0.1
95 1 0.0 0.1
97 1 0.0 0.1
NA 5073 86.9 NA
Total 5839 100.0 100.0 
# Stomach ulcers
  b4ha <- as.factor(d[,"b4ha"])
  # Make "*" to NA
b4ha[which(b4ha=="*")]<-"NA"
  levels(b4ha) <- list(No="1",
                     Yes="2")
  b4ha <- ordered(b4ha, c("No", "Yes"))
  
  new.d <- data.frame(new.d, b4ha)
  new.d <- apply_labels(new.d, b4ha = "Stomach ulcers")
  temp.d <- data.frame (new.d, b4ha)  
  
  result<-questionr::freq(temp.d$b4ha, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Stomach ulcers")
Stomach ulcers
n % val%
No 5190 88.9 91.7
Yes 467 8.0 8.3
NA 182 3.1 NA
Total 5839 100.0 100.0 
  b4hb <- as.factor(d[,"b4hb"])
  new.d <- data.frame(new.d, b4hb)
  new.d <- apply_labels(new.d, b4hb = "Stomach ulcers age")
  temp.d <- data.frame (new.d, b4hb)  
  result<-questionr::freq(temp.d$b4hb, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Stomach ulcers Age")
Stomach ulcers Age
n % val%
0 78 1.3 16.2
10 3 0.1 0.6
13 3 0.1 0.6
14 3 0.1 0.6
15 3 0.1 0.6
16 7 0.1 1.5
17 2 0.0 0.4
18 6 0.1 1.2
19 3 0.1 0.6
2 2 0.0 0.4
20 6 0.1 1.2
21 3 0.1 0.6
22 7 0.1 1.5
23 7 0.1 1.5
24 5 0.1 1.0
25 17 0.3 3.5
27 5 0.1 1.0
28 7 0.1 1.5
3 2 0.0 0.4
30 21 0.4 4.4
31 2 0.0 0.4
32 5 0.1 1.0
33 1 0.0 0.2
34 3 0.1 0.6
35 23 0.4 4.8
36 5 0.1 1.0
37 2 0.0 0.4
38 4 0.1 0.8
39 4 0.1 0.8
40 22 0.4 4.6
41 3 0.1 0.6
42 4 0.1 0.8
43 3 0.1 0.6
44 2 0.0 0.4
45 27 0.5 5.6
46 3 0.1 0.6
47 3 0.1 0.6
48 7 0.1 1.5
49 2 0.0 0.4
50 24 0.4 5.0
51 1 0.0 0.2
52 6 0.1 1.2
53 2 0.0 0.4
54 3 0.1 0.6
55 9 0.2 1.9
56 4 0.1 0.8
57 6 0.1 1.2
58 6 0.1 1.2
59 4 0.1 0.8
60 17 0.3 3.5
61 3 0.1 0.6
62 12 0.2 2.5
63 8 0.1 1.7
64 5 0.1 1.0
65 8 0.1 1.7
66 3 0.1 0.6
67 8 0.1 1.7
68 6 0.1 1.2
69 4 0.1 0.8
7 1 0.0 0.2
70 5 0.1 1.0
71 3 0.1 0.6
72 5 0.1 1.0
73 2 0.0 0.4
74 2 0.0 0.4
76 3 0.1 0.6
8 1 0.0 0.2
80 1 0.0 0.2
83 1 0.0 0.2
9 2 0.0 0.4
94 1 0.0 0.2
NA 5358 91.8 NA
Total 5839 100.0 100.0 
# Crohn's Disease
  b4ia <- as.factor(d[,"b4ia"])
  # Make "*" to NA
b4ia[which(b4ia=="*")]<-"NA"
  levels(b4ia) <- list(No="1",
                     Yes="2")
  b4ia <- ordered(b4ia, c("No", "Yes"))
  
  new.d <- data.frame(new.d, b4ia)
  new.d <- apply_labels(new.d, b4ia = "Crohn's Disease")
  temp.d <- data.frame (new.d, b4ia)  
  
  result<-questionr::freq(temp.d$b4ia, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Crohn's Disease")
Crohn’s Disease
n % val%
No 5435 93.1 96.4
Yes 204 3.5 3.6
NA 200 3.4 NA
Total 5839 100.0 100.0 
  b4ib <- as.factor(d[,"b4ib"])
  new.d <- data.frame(new.d, b4ib)
  new.d <- apply_labels(new.d, b4ib = "Crohn's Disease age")
  temp.d <- data.frame (new.d, b4ib)  
  result<-questionr::freq(temp.d$b4ib, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Crohn's Disease Age")
Crohn’s Disease Age
n % val%
12 1 0.0 0.6
15 1 0.0 0.6
2 2 0.0 1.2
21 1 0.0 0.6
22 2 0.0 1.2
23 1 0.0 0.6
24 1 0.0 0.6
25 3 0.1 1.8
26 1 0.0 0.6
27 1 0.0 0.6
28 1 0.0 0.6
29 1 0.0 0.6
30 1 0.0 0.6
31 1 0.0 0.6
32 4 0.1 2.3
33 1 0.0 0.6
34 1 0.0 0.6
35 5 0.1 2.9
36 1 0.0 0.6
37 2 0.0 1.2
38 2 0.0 1.2
39 1 0.0 0.6
40 13 0.2 7.6
41 2 0.0 1.2
43 1 0.0 0.6
44 5 0.1 2.9
45 5 0.1 2.9
46 2 0.0 1.2
47 1 0.0 0.6
48 2 0.0 1.2
50 11 0.2 6.4
51 3 0.1 1.8
52 3 0.1 1.8
53 2 0.0 1.2
54 3 0.1 1.8
55 11 0.2 6.4
56 4 0.1 2.3
57 2 0.0 1.2
58 7 0.1 4.1
59 3 0.1 1.8
60 8 0.1 4.7
62 3 0.1 1.8
63 6 0.1 3.5
64 3 0.1 1.8
65 5 0.1 2.9
66 4 0.1 2.3
67 2 0.0 1.2
69 6 0.1 3.5
70 5 0.1 2.9
71 4 0.1 2.3
74 1 0.0 0.6
75 3 0.1 1.8
76 2 0.0 1.2
77 1 0.0 0.6
79 2 0.0 1.2
85 1 0.0 0.6
NA 5668 97.1 NA
Total 5839 100.0 100.0 
# Diabetes
  b4ja <- as.factor(d[,"b4ja"])
  # Make "*" to NA
b4ja[which(b4ja=="*")]<-"NA"
  levels(b4ja) <- list(No="1",
                     Yes="2")
  b4ja <- ordered(b4ja, c("No", "Yes"))
  
  new.d <- data.frame(new.d, b4ja)
  new.d <- apply_labels(new.d, b4ja = "Diabetes")
  temp.d <- data.frame (new.d, b4ja)  
  
  result<-questionr::freq(temp.d$b4ja, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Diabetes")
Diabetes
n % val%
No 3949 67.6 69.2
Yes 1754 30.0 30.8
NA 136 2.3 NA
Total 5839 100.0 100.0 
  b4jb <- as.factor(d[,"b4jb"])
  new.d <- data.frame(new.d, b4jb)
  new.d <- apply_labels(new.d, b4jb = "Diabetes age")
  temp.d <- data.frame (new.d, b4jb)  
  result<-questionr::freq(temp.d$b4jb, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Diabetes Age")
Diabetes Age
n % val%
0 10 0.2 0.7
10 1 0.0 0.1
12 2 0.0 0.1
13 2 0.0 0.1
14 2 0.0 0.1
15 2 0.0 0.1
16 1 0.0 0.1
17 5 0.1 0.3
18 1 0.0 0.1
19 2 0.0 0.1
2 3 0.1 0.2
20 4 0.1 0.3
22 2 0.0 0.1
24 2 0.0 0.1
27 1 0.0 0.1
28 4 0.1 0.3
29 8 0.1 0.5
3 2 0.0 0.1
30 25 0.4 1.7
31 1 0.0 0.1
32 2 0.0 0.1
34 2 0.0 0.1
35 38 0.7 2.5
36 5 0.1 0.3
37 5 0.1 0.3
38 13 0.2 0.9
39 9 0.2 0.6
40 72 1.2 4.8
41 9 0.2 0.6
42 23 0.4 1.5
43 15 0.3 1.0
44 17 0.3 1.1
45 74 1.3 4.9
46 15 0.3 1.0
47 18 0.3 1.2
48 24 0.4 1.6
49 18 0.3 1.2
5 1 0.0 0.1
50 138 2.4 9.1
51 27 0.5 1.8
52 33 0.6 2.2
53 34 0.6 2.2
54 35 0.6 2.3
55 120 2.1 7.9
56 41 0.7 2.7
57 34 0.6 2.2
58 60 1.0 4.0
59 37 0.6 2.4
6 1 0.0 0.1
60 115 2.0 7.6
61 43 0.7 2.8
62 60 1.0 4.0
63 25 0.4 1.7
64 34 0.6 2.2
65 67 1.1 4.4
66 22 0.4 1.5
67 18 0.3 1.2
68 20 0.3 1.3
69 24 0.4 1.6
70 38 0.7 2.5
71 8 0.1 0.5
72 9 0.2 0.6
73 2 0.0 0.1
74 4 0.1 0.3
75 9 0.2 0.6
76 3 0.1 0.2
77 1 0.0 0.1
78 3 0.1 0.2
79 1 0.0 0.1
80 1 0.0 0.1
81 1 0.0 0.1
89 1 0.0 0.1
9 2 0.0 0.1
94 1 0.0 0.1
95 1 0.0 0.1
97 1 0.0 0.1
NA 4325 74.1 NA
Total 5839 100.0 100.0 
# Kidney Problems
  b4ka <- as.factor(d[,"b4ka"])
  # Make "*" to NA
b4ka[which(b4ka=="*")]<-"NA"
  levels(b4ka) <- list(No="1",
                     Yes="2")
  b4ka <- ordered(b4ka, c("No", "Yes"))
  
  new.d <- data.frame(new.d, b4ka)
  new.d <- apply_labels(new.d, b4ka = "Kidney Problems")
  temp.d <- data.frame (new.d, b4ka)  
  
  result<-questionr::freq(temp.d$b4ka, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Kidney Problems")
Kidney Problems
n % val%
No 5366 91.9 94.2
Yes 328 5.6 5.8
NA 145 2.5 NA
Total 5839 100.0 100.0 
  b4kb <- as.factor(d[,"b4kb"])
  new.d <- data.frame(new.d, b4kb)
  new.d <- apply_labels(new.d, b4kb = "Kidney Problems age")
  temp.d <- data.frame (new.d, b4kb)  
  result<-questionr::freq(temp.d$b4kb, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Kidney Problems Age")
Kidney Problems Age
n % val%
1 1 0.0 0.4
11 1 0.0 0.4
15 4 0.1 1.5
16 1 0.0 0.4
17 1 0.0 0.4
20 1 0.0 0.4
24 1 0.0 0.4
29 1 0.0 0.4
3 1 0.0 0.4
30 1 0.0 0.4
32 1 0.0 0.4
34 1 0.0 0.4
35 3 0.1 1.1
38 1 0.0 0.4
40 8 0.1 3.0
42 1 0.0 0.4
44 3 0.1 1.1
45 4 0.1 1.5
46 2 0.0 0.8
47 2 0.0 0.8
48 4 0.1 1.5
49 1 0.0 0.4
5 2 0.0 0.8
50 14 0.2 5.3
51 4 0.1 1.5
52 3 0.1 1.1
53 7 0.1 2.7
54 9 0.2 3.4
55 14 0.2 5.3
56 6 0.1 2.3
57 8 0.1 3.0
58 7 0.1 2.7
59 6 0.1 2.3
60 13 0.2 4.9
61 9 0.2 3.4
62 13 0.2 4.9
63 5 0.1 1.9
64 9 0.2 3.4
65 16 0.3 6.1
66 6 0.1 2.3
67 9 0.2 3.4
68 9 0.2 3.4
69 6 0.1 2.3
7 1 0.0 0.4
70 12 0.2 4.6
71 3 0.1 1.1
72 5 0.1 1.9
73 2 0.0 0.8
74 5 0.1 1.9
75 6 0.1 2.3
76 2 0.0 0.8
77 4 0.1 1.5
78 1 0.0 0.4
80 1 0.0 0.4
83 1 0.0 0.4
95 1 0.0 0.4
NA 5576 95.5 NA
Total 5839 100.0 100.0 
# Cirrhosis, liver damage
  b4la <- as.factor(d[,"b4la"])
  # Make "*" to NA
b4la[which(b4la=="*")]<-"NA"
  levels(b4la) <- list(No="1",
                     Yes="2")
  b4la <- ordered(b4la, c("No", "Yes"))
  
  new.d <- data.frame(new.d, b4la)
  new.d <- apply_labels(new.d, b4la = "Cirrhosis, liver damage")
  temp.d <- data.frame (new.d, b4la)  
  
  result<-questionr::freq(temp.d$b4la, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Cirrhosis, liver damage")
Cirrhosis, liver damage
n % val%
No 5565 95.3 98.1
Yes 109 1.9 1.9
NA 165 2.8 NA
Total 5839 100.0 100.0 
  b4lb <- as.factor(d[,"b4lb"])
  new.d <- data.frame(new.d, b4lb)
  new.d <- apply_labels(new.d, b4lb = "Cirrhosis, liver damage age")
  temp.d <- data.frame (new.d, b4lb)  
  result<-questionr::freq(temp.d$b4lb, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Cirrhosis, liver damage Age")
Cirrhosis, liver damage Age
n % val%
18 1 0.0 1.2
21 1 0.0 1.2
25 1 0.0 1.2
3 1 0.0 1.2
34 1 0.0 1.2
35 1 0.0 1.2
39 1 0.0 1.2
40 2 0.0 2.5
42 1 0.0 1.2
43 3 0.1 3.7
44 1 0.0 1.2
45 6 0.1 7.4
47 1 0.0 1.2
48 2 0.0 2.5
49 1 0.0 1.2
50 5 0.1 6.2
51 3 0.1 3.7
52 1 0.0 1.2
53 1 0.0 1.2
54 2 0.0 2.5
55 4 0.1 4.9
56 1 0.0 1.2
57 1 0.0 1.2
58 1 0.0 1.2
60 11 0.2 13.6
61 1 0.0 1.2
63 1 0.0 1.2
64 1 0.0 1.2
65 5 0.1 6.2
66 4 0.1 4.9
67 3 0.1 3.7
68 2 0.0 2.5
69 4 0.1 4.9
70 1 0.0 1.2
73 1 0.0 1.2
74 2 0.0 2.5
77 1 0.0 1.2
81 1 0.0 1.2
NA 5758 98.6 NA
Total 5839 100.0 100.0 
# Arthritis
  b4ma <- as.factor(d[,"b4ma"])
  # Make "*" to NA
b4ma[which(b4ma=="*")]<-"NA"
  levels(b4ma) <- list(No="1",
                     Yes="2")
  b4ma <- ordered(b4ma, c("No", "Yes"))
  
  new.d <- data.frame(new.d, b4ma)
  new.d <- apply_labels(new.d, b4ma = "Arthritis")
  temp.d <- data.frame (new.d, b4ma)  
  
  result<-questionr::freq(temp.d$b4ma, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Arthritis")
Arthritis
n % val%
No 4977 85.2 87.8
Yes 691 11.8 12.2
NA 171 2.9 NA
Total 5839 100.0 100.0 
  b4mb <- as.factor(d[,"b4mb"])
  new.d <- data.frame(new.d, b4mb)
  new.d <- apply_labels(new.d, b4mb = "Arthritis age")
  temp.d <- data.frame (new.d, b4mb)  
  result<-questionr::freq(temp.d$b4mb, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Arthritis Age")
Arthritis Age
n % val%
1 1 0.0 0.2
10 1 0.0 0.2
15 1 0.0 0.2
17 5 0.1 0.9
18 1 0.0 0.2
19 1 0.0 0.2
2 1 0.0 0.2
20 4 0.1 0.7
21 2 0.0 0.3
22 1 0.0 0.2
23 2 0.0 0.3
24 1 0.0 0.2
25 3 0.1 0.5
28 2 0.0 0.3
30 15 0.3 2.6
32 5 0.1 0.9
33 1 0.0 0.2
34 1 0.0 0.2
35 15 0.3 2.6
36 7 0.1 1.2
38 4 0.1 0.7
39 5 0.1 0.9
4 2 0.0 0.3
40 30 0.5 5.1
41 6 0.1 1.0
42 6 0.1 1.0
43 6 0.1 1.0
44 6 0.1 1.0
45 30 0.5 5.1
46 2 0.0 0.3
47 4 0.1 0.7
48 10 0.2 1.7
49 4 0.1 0.7
50 55 0.9 9.4
51 9 0.2 1.5
52 13 0.2 2.2
53 10 0.2 1.7
54 13 0.2 2.2
55 39 0.7 6.6
56 12 0.2 2.0
57 10 0.2 1.7
58 16 0.3 2.7
59 8 0.1 1.4
60 62 1.1 10.5
61 8 0.1 1.4
62 20 0.3 3.4
63 14 0.2 2.4
64 14 0.2 2.4
65 28 0.5 4.8
66 7 0.1 1.2
67 10 0.2 1.7
68 8 0.1 1.4
69 6 0.1 1.0
7 1 0.0 0.2
70 15 0.3 2.6
71 4 0.1 0.7
72 5 0.1 0.9
73 2 0.0 0.3
74 2 0.0 0.3
75 4 0.1 0.7
76 1 0.0 0.2
77 2 0.0 0.3
78 1 0.0 0.2
80 1 0.0 0.2
81 1 0.0 0.2
85 1 0.0 0.2
94 1 0.0 0.2
NA 5251 89.9 NA
Total 5839 100.0 100.0 
# Dementia
  b4na <- as.factor(d[,"b4na"])
  # Make "*" to NA
b4na[which(b4na=="*")]<-"NA"
  levels(b4na) <- list(No="1",
                     Yes="2")
  b4na <- ordered(b4na, c("No", "Yes"))
  
  new.d <- data.frame(new.d, b4na)
  new.d <- apply_labels(new.d, b4na = "Dementia")
  temp.d <- data.frame (new.d, b4na)  
  
  result<-questionr::freq(temp.d$b4na, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Dementia")
Dementia
n % val%
No 5632 96.5 98.8
Yes 68 1.2 1.2
NA 139 2.4 NA
Total 5839 100.0 100.0 
  b4nb <- as.factor(d[,"b4nb"])
  new.d <- data.frame(new.d, b4nb)
  new.d <- apply_labels(new.d, b4nb = "Dementia age")
  temp.d <- data.frame (new.d, b4nb)  
  result<-questionr::freq(temp.d$b4nb, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Dementia Age")
Dementia Age
n % val%
10 1 0.0 2.2
29 1 0.0 2.2
53 1 0.0 2.2
57 1 0.0 2.2
59 2 0.0 4.4
60 1 0.0 2.2
62 3 0.1 6.7
63 3 0.1 6.7
64 1 0.0 2.2
65 2 0.0 4.4
66 1 0.0 2.2
67 1 0.0 2.2
68 1 0.0 2.2
69 1 0.0 2.2
70 10 0.2 22.2
72 4 0.1 8.9
73 2 0.0 4.4
74 1 0.0 2.2
75 4 0.1 8.9
79 1 0.0 2.2
80 1 0.0 2.2
84 1 0.0 2.2
85 1 0.0 2.2
NA 5794 99.2 NA
Total 5839 100.0 100.0 
# Depression 
  b4oa <- as.factor(d[,"b4oa"])
  # Make "*" to NA
b4oa[which(b4oa=="*")]<-"NA"
  levels(b4oa) <- list(No="1",
                     Yes="2")
  b4oa <- ordered(b4oa, c("No", "Yes"))
  
  new.d <- data.frame(new.d, b4oa)
  new.d <- apply_labels(new.d, b4oa = "Depression")
  temp.d <- data.frame (new.d, b4oa)  
  
  result<-questionr::freq(temp.d$b4oa, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Depression")
Depression
n % val%
No 5002 85.7 88.1
Yes 676 11.6 11.9
NA 161 2.8 NA
Total 5839 100.0 100.0 
  b4ob <- as.factor(d[,"b4ob"])
  new.d <- data.frame(new.d, b4ob)
  new.d <- apply_labels(new.d, b4ob = "Depression age")
  temp.d <- data.frame (new.d, b4ob)  
  result<-questionr::freq(temp.d$b4ob, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Depression Age")
Depression Age
n % val%
1 1 0.0 0.2
10 1 0.0 0.2
14 1 0.0 0.2
15 1 0.0 0.2
16 1 0.0 0.2
17 1 0.0 0.2
18 1 0.0 0.2
19 8 0.1 1.5
20 10 0.2 1.9
21 7 0.1 1.3
22 2 0.0 0.4
23 1 0.0 0.2
24 1 0.0 0.2
25 10 0.2 1.9
26 3 0.1 0.6
27 1 0.0 0.2
28 9 0.2 1.7
29 2 0.0 0.4
3 1 0.0 0.2
30 10 0.2 1.9
31 1 0.0 0.2
32 6 0.1 1.1
33 2 0.0 0.4
34 3 0.1 0.6
35 20 0.3 3.8
36 5 0.1 1.0
37 4 0.1 0.8
38 7 0.1 1.3
39 2 0.0 0.4
4 1 0.0 0.2
40 23 0.4 4.4
41 3 0.1 0.6
42 10 0.2 1.9
43 8 0.1 1.5
44 6 0.1 1.1
45 18 0.3 3.4
46 7 0.1 1.3
47 8 0.1 1.5
48 13 0.2 2.5
49 7 0.1 1.3
5 1 0.0 0.2
50 41 0.7 7.8
51 7 0.1 1.3
52 12 0.2 2.3
53 8 0.1 1.5
54 8 0.1 1.5
55 27 0.5 5.2
56 8 0.1 1.5
57 12 0.2 2.3
58 15 0.3 2.9
59 7 0.1 1.3
6 1 0.0 0.2
60 26 0.4 5.0
61 12 0.2 2.3
62 9 0.2 1.7
63 6 0.1 1.1
64 7 0.1 1.3
65 23 0.4 4.4
66 10 0.2 1.9
67 4 0.1 0.8
68 13 0.2 2.5
69 6 0.1 1.1
7 2 0.0 0.4
70 14 0.2 2.7
72 3 0.1 0.6
73 4 0.1 0.8
74 2 0.0 0.4
75 1 0.0 0.2
76 2 0.0 0.4
79 1 0.0 0.2
8 1 0.0 0.2
9 1 0.0 0.2
91 1 0.0 0.2
96 1 0.0 0.2
98 2 0.0 0.4
NA 5315 91.0 NA
Total 5839 100.0 100.0 
# AIDS
  b4pa <- as.factor(d[,"b4pa"])
  # Make "*" to NA
b4pa[which(b4pa=="*")]<-"NA"
  levels(b4pa) <- list(No="1",
                     Yes="2")
  b4pa <- ordered(b4pa, c("No", "Yes"))
  
  new.d <- data.frame(new.d, b4pa)
  new.d <- apply_labels(new.d, b4pa = "AIDS")
  temp.d <- data.frame (new.d, b4pa)  
  
  result<-questionr::freq(temp.d$b4pa, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "AIDS")
AIDS
n % val%
No 5604 96.0 98.8
Yes 69 1.2 1.2
NA 166 2.8 NA
Total 5839 100.0 100.0 
  b4pb <- as.factor(d[,"b4pb"])
  new.d <- data.frame(new.d, b4pb)
  new.d <- apply_labels(new.d, b4pb = "AIDS age")
  temp.d <- data.frame (new.d, b4pb)  
  result<-questionr::freq(temp.d$b4pb, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "AIDS Age")
AIDS Age
n % val%
0 82 1.4 62.1
25 2 0.0 1.5
28 1 0.0 0.8
30 3 0.1 2.3
31 1 0.0 0.8
32 1 0.0 0.8
33 1 0.0 0.8
35 2 0.0 1.5
36 2 0.0 1.5
38 1 0.0 0.8
39 1 0.0 0.8
40 4 0.1 3.0
42 1 0.0 0.8
44 1 0.0 0.8
45 2 0.0 1.5
47 1 0.0 0.8
48 2 0.0 1.5
49 2 0.0 1.5
50 4 0.1 3.0
51 2 0.0 1.5
54 1 0.0 0.8
55 2 0.0 1.5
59 2 0.0 1.5
60 3 0.1 2.3
61 1 0.0 0.8
63 1 0.0 0.8
65 1 0.0 0.8
66 1 0.0 0.8
69 1 0.0 0.8
7 1 0.0 0.8
9 1 0.0 0.8
90 1 0.0 0.8
NA 5707 97.7 NA
Total 5839 100.0 100.0 
# Other Cancer
  b4qa <- as.factor(d[,"b4qa"])
  # Make "*" to NA
b4qa[which(b4qa=="*")]<-"NA"
  levels(b4qa) <- list(No="1",
                     Yes="2")
  b4qa <- ordered(b4qa, c("No", "Yes"))
  
  new.d <- data.frame(new.d, b4qa)
  new.d <- apply_labels(new.d, b4qa = "Other Cancer")
  temp.d <- data.frame (new.d, b4qa)  
  
  result<-questionr::freq(temp.d$b4qa, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Other Cancer")
Other Cancer
n % val%
No 5204 89.1 93.3
Yes 376 6.4 6.7
NA 259 4.4 NA
Total 5839 100.0 100.0 
  b4qb <- as.factor(d[,"b4qb"])
  new.d <- data.frame(new.d, b4qb)
  new.d <- apply_labels(new.d, b4qb = "Other Cancer age")
  temp.d <- data.frame (new.d, b4qb)  
  result<-questionr::freq(temp.d$b4qb, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Other Cancer Age")
Other Cancer Age
n % val%
08 1 0.0 0.3
10 1 0.0 0.3
12 1 0.0 0.3
16 1 0.0 0.3
18 2 0.0 0.6
19 2 0.0 0.6
2 1 0.0 0.3
20 1 0.0 0.3
21 1 0.0 0.3
22 1 0.0 0.3
23 1 0.0 0.3
24 1 0.0 0.3
3 1 0.0 0.3
30 3 0.1 0.9
32 1 0.0 0.3
35 1 0.0 0.3
36 1 0.0 0.3
38 1 0.0 0.3
39 1 0.0 0.3
40 3 0.1 0.9
41 2 0.0 0.6
42 4 0.1 1.2
43 4 0.1 1.2
44 7 0.1 2.1
45 2 0.0 0.6
47 2 0.0 0.6
48 3 0.1 0.9
49 9 0.2 2.7
50 10 0.2 3.0
51 4 0.1 1.2
52 3 0.1 0.9
53 5 0.1 1.5
54 11 0.2 3.3
55 14 0.2 4.2
56 14 0.2 4.2
57 7 0.1 2.1
58 20 0.3 6.0
59 6 0.1 1.8
60 23 0.4 6.9
61 15 0.3 4.5
62 12 0.2 3.6
63 13 0.2 3.9
64 9 0.2 2.7
65 12 0.2 3.6
66 10 0.2 3.0
67 9 0.2 2.7
68 13 0.2 3.9
69 11 0.2 3.3
7 1 0.0 0.3
70 11 0.2 3.3
71 5 0.1 1.5
72 7 0.1 2.1
73 5 0.1 1.5
74 8 0.1 2.4
75 3 0.1 0.9
76 4 0.1 1.2
77 2 0.0 0.6
78 2 0.0 0.6
79 1 0.0 0.3
80 2 0.0 0.6
81 2 0.0 0.6
87 1 0.0 0.3
9 1 0.0 0.3
NA 5504 94.3 NA
Total 5839 100.0 100.0

B4Q Other Cancer

b4qother <- d[,"b4qother"]
  new.d <- data.frame(new.d, b4qother)
  new.d <- apply_labels(new.d, b4qother = "b4qother")
  temp.d <- data.frame (new.d, b4qother)
result<-questionr::freq(temp.d$b4qother, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B4Q Other")
B4Q Other
n % val%
#NAME? 1 0.0 0.3
3rd stage lung cancer 1 0.0 0.3
AML. 1 0.0 0.3
Anal 1 0.0 0.3
Anal cancer 1 0.0 0.3
Anemia. 1 0.0 0.3
Benign melanoma 1 0.0 0.3
Bladder 10 0.2 2.8
Bladder cancer 3 0.1 0.8
Bladder Cancer 2 0.0 0.6
Bladder cancer. 2 0.0 0.6
Bladder. 1 0.0 0.3
Blood cancer 1 0.0 0.3
Bone 1 0.0 0.3
Bone cancer 1 0.0 0.3
Bone marrow 1 0.0 0.3
Bone marrow cancer 1 0.0 0.3
Bone marrow cancer. 1 0.0 0.3
Bone. 2 0.0 0.6
Breast 1 0.0 0.3
breast cancer 1 0.0 0.3
Breast cancer 2 0.0 0.6
Cancer is now affecting my bones. 1 0.0 0.3
Cancer of my vocal cords 1 0.0 0.3
Cancerous colon polyp. 1 0.0 0.3
Cancerous tumor on right kidney, 2 centimeters cut away. 1 0.0 0.3
Cancerous tumor on right Kidney, kidney removed 1 0.0 0.3
Check now on bile ducts. 1 0.0 0.3
Chronic lymphocytic leukemia 1 0.0 0.3
CLL 3 0.1 0.8
CLL 2018/Lung cancer 1 0.0 0.3
CLL. 1 0.0 0.3
CML Chronic Myeloid Leukemia 1 0.0 0.3
CML, Polymyalgia Rheumatic bladder, and Amyloidosis of urinary bladder 1 0.0 0.3
CML. 1 0.0 0.3
CO 1 0.0 0.3
Colo rectal cancer 1 0.0 0.3
Colo-rectal Cancer 1 0.0 0.3
colon 3 0.1 0.8
Colon 30 0.5 8.4
Colon (2000). 1 0.0 0.3
Colon (polyps) 1 0.0 0.3
Colon (self) 1 0.0 0.3
Colon and left and right kidney 1 0.0 0.3
colon cancer 1 0.0 0.3
Colon cancer 9 0.2 2.5
Colon Cancer 3 0.1 0.8
Colon cancer. 3 0.1 0.8
Colon Dr. removed it and that’s over 1 0.0 0.3
Colon. 2 0.0 0.6
Colorectal 2 0.0 0.6
Colorectal cancer. 1 0.0 0.3
COPD 2 0.0 0.6
Do not know dates on prior questions 1 0.0 0.3
Duodenum. 1 0.0 0.3
esophageal 1 0.0 0.3
Esophageal 1 0.0 0.3
esophagus 1 0.0 0.3
Esophagus, lung cancer 1 0.0 0.3
GIST 1 0.0 0.3
Glast cancer 1 0.0 0.3
Glaucoma cancer 1 0.0 0.3
Growth on bladder (unknown) 1 0.0 0.3
Growth on Kidney 1 0.0 0.3
Has asbestos 1 0.0 0.3
Head and neck (throat). 1 0.0 0.3
Head and Neck (Tounge cancer in 2007) 1 0.0 0.3
Hepatitis C 1 0.0 0.3
HIV 1 0.0 0.3
HIV positive 1 0.0 0.3
HIV positive, no detected 1 0.0 0.3
HIV. 1 0.0 0.3
HPV 1 0.0 0.3
Hypertropic Bowen’s disease 1 0.0 0.3
I do not know, maybe 1 0.0 0.3
In the family (call me). 1 0.0 0.3
Invasive squamous cell carcinoma (on finger-amputation of distal portion of LF. index finger). 1 0.0 0.3
Kalsom 1 0.0 0.3
Kaposis Sarcoma 1 0.0 0.3
kidney 2 0.0 0.6
Kidney 16 0.3 4.5
KIDNEY 1 0.0 0.3
Kidney (left kidney removed). 1 0.0 0.3
kidney cancer 1 0.0 0.3
Kidney cancer 3 0.1 0.8
Kidney Cancer 6 0.1 1.7
Kidney cancer in 2016 1 0.0 0.3
Kidney cancer. 2 0.0 0.6
Kidney had kidney ablasion. 1 0.0 0.3
Kidney tumor 1 0.0 0.3
Kidney-colon. 1 0.0 0.3
Kidney-partial nephrectomy. 1 0.0 0.3
Kidney-prostate 1 0.0 0.3
Kidney. 4 0.1 1.1
Left kidney 1 0.0 0.3
Left kidney was taken (believe to be cancerous) after taken and tested it was not cancerous. So I have only one now, right side. 1 0.0 0.3
Left kidney. 1 0.0 0.3
Leukemia 2 0.0 0.6
Leukemia (CML). 1 0.0 0.3
Liver 3 0.1 0.8
Liver cancer colon cancer 1 0.0 0.3
Liver had transplant 71 years 1 0.0 0.3
Liver mass (7-11-14) 1 0.0 0.3
LMS 1 0.0 0.3
Lt. lung removed 97. 1 0.0 0.3
Lump in right side of neck 1 0.0 0.3
lung 3 0.1 0.8
Lung 14 0.2 3.9
Lung (left 2016) right-present 1 0.0 0.3
lung cancer 1 0.0 0.3
Lung cancer 5 0.1 1.4
Lung cancer-stage 4. 1 0.0 0.3
Lung cancer. 1 0.0 0.3
Lung caner 1 0.0 0.3
Lung, —- on kidney, prostate 1 0.0 0.3
Lung, esophagus 1 0.0 0.3
Lung/GIST tumor. 1 0.0 0.3
Lupus 1 0.0 0.3
Lymphoid cancer stage 4. 1 0.0 0.3
Lymphoma 7 0.1 2.0
Lymphoma 1999-leukemia 2021. 1 0.0 0.3
Lymphoma low grade 1 0.0 0.3
Lymphoma. 1 0.0 0.3
Lymphoma/Colon 1 0.0 0.3
lyphama 1 0.0 0.3
MAL and Lymphoma 1 0.0 0.3
Malignant tumor in bladder. 1 0.0 0.3
MDS 1 0.0 0.3
Melanoma 2 0.0 0.6
Meningioma 1 0.0 0.3
Metastatic Papillary Thyroid Cancer 1 0.0 0.3
MPD and AML 1 0.0 0.3
Multiple myeloma 1 0.0 0.3
multiple myeloma 1 0.0 0.3
Multiple myeloma 1 0.0 0.3
Multiple Myeloma 7 0.1 2.0
Multiple Myloma 1 0.0 0.3
My left kidney was removed because a mass was found on it in April 2018, it was cancer, my kidney was removed on May 1, 2018. 1 0.0 0.3
My sister at 63, colon. 1 0.0 0.3
Myeloma 1 0.0 0.3
Myloma kidney 1 0.0 0.3
Nasal pharyngeal 1 0.0 0.3
Neck cancer 1 0.0 0.3
Neuroendocrine (Carcinoma) 1 0.0 0.3
Neuroendocrine cancer 1 0.0 0.3
NH Lymphoma, skin cancer 1 0.0 0.3
No 2 0.0 0.6
Non Hodgkin’s Lymphoma (CLL) 1 0.0 0.3
Non Hodgkins Lymphoma 1 0.0 0.3
Non-Hodgekins Lymphoma. 1 0.0 0.3
non-Hodgkin lymphoma 1 0.0 0.3
Non-Hodgkin Lymphoma 1 0.0 0.3
Non-Hodgkin’s lymphoma 1 0.0 0.3
Non-Hodgkin’s Lymphoma 1 0.0 0.3
Non-Hodgkins Lymphoma (2) 1 0.0 0.3
None. 1 0.0 0.3
Oral 1 0.0 0.3
Over active white blood cell 1 0.0 0.3
Pancreatic 2 0.0 0.6
Pancreatic and liver 1 0.0 0.3
Partial kidney removal right side 1 0.0 0.3
Partial left kidney removal 1 0.0 0.3
Pelvis and lymphoma 1 0.0 0.3
Penile Cancer 1 0.0 0.3
Posterial Sarcoma 1 0.0 0.3
Prostate 1 0.0 0.3
Prostate only 1 0.0 0.3
Rectal 1 0.0 0.3
Rectal cancer 1 0.0 0.3
Refused! 1 0.0 0.3
Renal kidney remove left side 1 0.0 0.3
Results by Dr. Libby 2018 no cancer found! 1 0.0 0.3
right kidney 1 0.0 0.3
Right kidney 1 0.0 0.3
Right kidney (neprectomy). 1 0.0 0.3
Right kidney nephrectomy 1 0.0 0.3
Right vocal cord 1 0.0 0.3
Salivary gland cancer. 1 0.0 0.3
Sarcoma-Soft tissue cancer 1 0.0 0.3
sinus and right axilary muscle 1 0.0 0.3
Skin 2 0.0 0.6
Skin cancer 4 0.1 1.1
Skin cancer stomach area 1 0.0 0.3
Skin Cancer, Breast Cancer 1 0.0 0.3
Skin cancer. 2 0.0 0.6
Skin Cancer/side effect from Kertruda treatment 1 0.0 0.3
Small cell Lung cancer 1 0.0 0.3
Small tumor on brain 1 0.0 0.3
Spread to liver and pelvic area. 1 0.0 0.3
Squamous Cell Carcinoma 1 0.0 0.3
Stage 4 lung cancer 1 0.0 0.3
Stomach cancer 1 0.0 0.3
Stomach cancer. 1 0.0 0.3
Stomach. 2 0.0 0.6
T cell lymphoma 1 0.0 0.3
testicular 1 0.0 0.3
Testicular 1 0.0 0.3
Thank God, I don’t need more 1 0.0 0.3
There was a nodule on my lungs It was removed it was cancer. 1 0.0 0.3
Throat 6 0.1 1.7
Throat and tonsils 1 0.0 0.3
throat cancer 1 0.0 0.3
Throat cancer 1 0.0 0.3
Throat cancer, vocal chord cancer 1 0.0 0.3
thyroid 1 0.0 0.3
Thyroid 5 0.1 1.4
Thyroid gland disease 1 0.0 0.3
Thyroid. 2 0.0 0.6
Tumor cancer 1 0.0 0.3
Tumor in bladder 1 0.0 0.3
Tumor on my left kidney (Nephrectomy) 1 0.0 0.3
Unknown origin 1 0.0 0.3
Vocal cord 1 0.0 0.3
Waldenstrom Non Hodgkins Lymphoma 1 0.0 0.3
Yes, prostate cancer. 1 0.0 0.3
NA 5481 93.9 NA
Total 5839 100.0 100.0

B5: Routine care

  • B5. Where do you usually go for routine medical care (seeing a doctor for any reason, not just for cancer care)?
    • 1=Community health center or free clinic
    • 2=Hospital (not emergency)/ urgent care clinic
    • 3=Private doctor’s office
    • 4=Emergency room
    • 5=Veteran’s Affairs/VA
    • 6=Other type of location
  b5 <- as.factor(d[,"b5"])
# Make "*" to NA
b5[which(b5=="*")]<-"NA"
  levels(b5) <- list(Community_center_free_clinic="1",
                     Hospital_urgent_care_clinic="2",
                     Private_Dr_office="3",
                     ER="4",
                     VA="5",
                     Other="6")
  b5 <- ordered(b5, c("Community_center_free_clinic", "Hospital_urgent_care_clinic", "Private_Dr_office", "ER","VA","Other"))
  
  new.d <- data.frame(new.d, b5)
  new.d <- apply_labels(new.d, b5 = "routine medical care")
  temp.d <- data.frame (new.d, b5)  
  
  result<-questionr::freq(temp.d$b5 ,total = TRUE)
  kable(result, format = "simple", align = 'l')
n % val%
Community_center_free_clinic 380 6.5 7.1
Hospital_urgent_care_clinic 400 6.9 7.5
Private_Dr_office 3912 67.0 73.1
ER 37 0.6 0.7
VA 513 8.8 9.6
Other 108 1.8 2.0
NA 489 8.4 NA
Total 5839 100.0 100.0

B5 Other: Routine care

b5other <- d[,"b5other"]
  new.d <- data.frame(new.d, b5other)
  new.d <- apply_labels(new.d, b5other = "b5other")
  temp.d <- data.frame (new.d, b5other)
result<-questionr::freq(temp.d$b5other, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B5 Other")
B5 Other
n % val%
(Emory), Wellstar, family MD, Kennestone Family Medicine. 1 0.0 0.4
29 Palms Navel Hospital 1 0.0 0.4
A. Ahmad, MD PLC. 1 0.0 0.4
Advance Medical Mobile Care 1 0.0 0.4
Advanced Urology 1 0.0 0.4
Advocate Aurora Health facility 1 0.0 0.4
Aenta 1 0.0 0.4
Albany health care center 1 0.0 0.4
Alpha Medical 1 0.0 0.4
Also private doctor’s office 1 0.0 0.4
Atlanta cancer care/Northside hospital 1 0.0 0.4
Atlanta Heart Specialist 1 0.0 0.4
Austin Diagnostic Clinic 1 0.0 0.4
Back specialist. 1 0.0 0.4
Baylor Hospital Dr. Joshua Fines 1 0.0 0.4
Beaumont Doctors and VA 1 0.0 0.4
Beaumont Family Medicine. 1 0.0 0.4
Ben tab 1 0.0 0.4
Braselton Clinic, Braselton GA 1 0.0 0.4
Cancer center. 1 0.0 0.4
Cancer treatment of America (CTA). 1 0.0 0.4
CDC-Doctor 1 0.0 0.4
Cedar 1 0.0 0.4
Chabert Medical Center 1 0.0 0.4
Charleston SC Hinesville Cr. 1 0.0 0.4
CIS Diabetes Specialist 1 0.0 0.4
City of Hope 1 0.0 0.4
Clinic. 1 0.0 0.4
Company doctor 1 0.0 0.4
Crescent Care Primary Providers 1 0.0 0.4
CTCA Cancer Treatment Center 1 0.0 0.4
Davis Medical GPMC ST/CA. 1 0.0 0.4
Dialyse dialysis. 1 0.0 0.4
Dialysis center 1 0.0 0.4
Doctor affiliated with hospital 1 0.0 0.4
Doctor’s office located at hospital. 1 0.0 0.4
doctors office 1 0.0 0.4
Doctors office (primary Dr) 1 0.0 0.4
Dr. Alvin Griffin 1 0.0 0.4
Dr. Chomsky 1 0.0 0.4
Dr. Feldman (Oakland Family Practice office) 1 0.0 0.4
Dr. Harper 1 0.0 0.4
Dr. Karimjee 1 0.0 0.4
Dr. Mark Clark (Cape Charles Medical Center). 1 0.0 0.4
Dr. Marvin Crawford (Piedmont) 1 0.0 0.4
Dr. Ramiz. 1 0.0 0.4
Dr. Reed Laygo 1 0.0 0.4
Eagle Landing family practice 1 0.0 0.4
Eastmont Medical Center 1 0.0 0.4
EM room 1 0.0 0.4
Emomory Health Care La Grange GA 1 0.0 0.4
Emor Health Care 1 0.0 0.4
emory 1 0.0 0.4
Emory 1 0.0 0.4
Emory (PCP) 1 0.0 0.4
Emory Clinic 1 0.0 0.4
Emory Clinic. 1 0.0 0.4
Emory Clinic/Hospital 1 0.0 0.4
Emory hospital transplant center 1 0.0 0.4
Emory Midtown 1 0.0 0.4
Emory-Neurological unit for Alzheimer’s 1 0.0 0.4
Every month (I take Warfarin) 1 0.0 0.4
Eye Doctor/Eye sight associate 1 0.0 0.4
Facey Med Group 1 0.0 0.4
Family Doctor Emilio Cardona 313-841-7575 1 0.0 0.4
Family Health center 1 0.0 0.4
Family practice center 1 0.0 0.4
Family practice. 1 0.0 0.4
Fern Cave Medical Center 1 0.0 0.4
For kidney stone 1 0.0 0.4
FPT, Jacksonville, FL 1 0.0 0.4
Grady 1 0.0 0.4
Grady Health System. 1 0.0 0.4
Grady Hospital HTI. 1 0.0 0.4
Grady Memorial Purple Pod 1 0.0 0.4
Harper Professional. 1 0.0 0.4
Harris Health Clinics 1 0.0 0.4
Heart doctor 1 0.0 0.4
Henry Ford Clinic 1 0.0 0.4
Henry Ford Health System 1 0.0 0.4
Henry Ford hospital clinic 1 0.0 0.4
Henry Ford Hospital system Detroit Mi 1 0.0 0.4
Henry Ford Satellite 1 0.0 0.4
Home. 1 0.0 0.4
Hospital, community HC 1 0.0 0.4
Hospital. 1 0.0 0.4
Hunt Medical Center. 1 0.0 0.4
HWY 138 Riverdale GA 30296 1 0.0 0.4
I go to Kaiser hospital clinic, dedicated doctor. 1 0.0 0.4
I haven’t been checked over one year. 1 0.0 0.4
I’m retired from Army 1 0.0 0.4
In my doctors office 1 0.0 0.4
JBACC 1 0.0 0.4
Jen-care clinic 1 0.0 0.4
Jersey City Medical Center 1 0.0 0.4
Just switched to VA 1 0.0 0.4
Kaiser 9 0.2 3.6
Kaiser —- 1 0.0 0.4
Kaiser hospital 1 0.0 0.4
Kaiser Hospital 1 0.0 0.4
Kaiser Permanente 1 0.0 0.4
Kaiser Permanente, Fontana, CA. 1 0.0 0.4
Kaiser Permanente. 1 0.0 0.4
Kaiser Permanently 1 0.0 0.4
Kaiser. 2 0.0 0.8
Kaizer, San Francisco 1 0.0 0.4
Karmanos 1 0.0 0.4
Karmanos Cancer Institute 1 0.0 0.4
Karmanos 4100 John 1 0.0 0.4
Kaufman Clinic 1 0.0 0.4
Keck East Los Angeles 1 0.0 0.4
Kelsey 1 0.0 0.4
Kelsey Seybold Clinic 1 0.0 0.4
Kelsey Seybold. 1 0.0 0.4
Kirkwood Family Medicine 1 0.0 0.4
LA Care 1 0.0 0.4
LA care/Martin Luther King 1 0.0 0.4
LA Pain spe 1 0.0 0.4
Lab quest PSA levels every three months. 1 0.0 0.4
LGBTQ Center 1 0.0 0.4
Loma Linda Hospital 1 0.0 0.4
LSU Hospital 1 0.0 0.4
Medicaid 1 0.0 0.4
Medical cancer. 1 0.0 0.4
Medical center 1 0.0 0.4
Medicare 1 0.0 0.4
Medicare and medical —- 1 0.0 0.4
Medicate, Pulmonary doctor and urologist in my area 1 0.0 0.4
Memorial Hermann Private doctor 1 0.0 0.4
Mercer Medicine LLC. 1 0.0 0.4
Methodist Hospital 1 0.0 0.4
Military facility 1 0.0 0.4
Military Hospital 1 0.0 0.4
Morehouse/Grady 1 0.0 0.4
Moss Memorial Primary Care Clinic 1 0.0 0.4
MPTF (UCLA) 1 0.0 0.4
My doctor 1 0.0 0.4
My doctor, Pablo Splenser. 1 0.0 0.4
My regular doctor 1 0.0 0.4
Nephrologist 1 0.0 0.4
Neurologic 1 0.0 0.4
No. 2 0.0 0.8
None. 1 0.0 0.4
Not HMO 1 0.0 0.4
Oak Tree Health 1 0.0 0.4
Oakhurst medical center 1 0.0 0.4
Ochiner 1 0.0 0.4
Ochsner Baptist NOLA. 1 0.0 0.4
Ochsner Clinic 1 0.0 0.4
Ochsner of New Orleans 1 0.0 0.4
Ocshner Clinic 1 0.0 0.4
Ocshner Clinic/West Bank. 1 0.0 0.4
Open health care clinic 1 0.0 0.4
Otero County Regional Medical Center 1 0.0 0.4
Palvillon Houston Health Care 1 0.0 0.4
Park Plaza Hosp. 1313 Herman Dr. 77004 713-527-5000 1 0.0 0.4
Parkland Memorial Hospital 1 0.0 0.4
Parkland. 1 0.0 0.4
PCP office. 1 0.0 0.4
PCP, Dr. Vannoy 1 0.0 0.4
Primary Care 1 0.0 0.4
Primary Care / Anual checkup 1 0.0 0.4
Primary care doctor 1 0.0 0.4
Primary Care Doctor 1 0.0 0.4
PRIMARY CARE DOCTOR 1 0.0 0.4
Primary care doctor. 2 0.0 0.8
Primary care physician 1 0.0 0.4
Primary care-Dr. Williams 1 0.0 0.4
Primary Doc 1 0.0 0.4
Primary doctor 3 0.1 1.2
Primary doctor (People Health) 1 0.0 0.4
Primary doctor Dr. Zoghbi 1 0.0 0.4
primary doctor dr.guagrilia hewitt,nj 1 0.0 0.4
Primary doctor thru United Health Care 1 0.0 0.4
Primary doctors Rober —- 1 0.0 0.4
Primary Michelle Cooke 1 0.0 0.4
Primary physician. 1 0.0 0.4
Private civilian Doctor too including the VA clinic 1 0.0 0.4
Private clinics 1 0.0 0.4
Private doctor 1 0.0 0.4
Private Doctor 1 0.0 0.4
private doctor and the Harper professional building and Sinai-Grace hospital 1 0.0 0.4
Private doctor is 1 0.0 0.4
Private doctors office 1 0.0 0.4
Private doctors office 2200 Emery — Hill. 1 0.0 0.4
Private Dr. 1 0.0 0.4
Private Dr’s office 1 0.0 0.4
Prompt care 1 0.0 0.4
Reg. Dr. follow up 1 0.0 0.4
Rockdale Family Practice 1 0.0 0.4
SAG/AFTRA UCLA TOLUCA LAKE MEDICAL FACILITY 1 0.0 0.4
Santa Clara Family Health Plan. 1 0.0 0.4
Scan. 1 0.0 0.4
Scan/UCLA. 1 0.0 0.4
Sini Grace Professional Bldg 1 0.0 0.4
Southwell Clinic Hwy 41 N, Tifton, GA 31794 1 0.0 0.4
Southwell Medical Clinic 1 0.0 0.4
Specialist Care-UTSW Med. Ctr Dallas 1 0.0 0.4
St. Mary’s Livonia 1 0.0 0.4
Stanford Health Care-employer benefits 1 0.0 0.4
Stanford hospital Palo Alto 1 0.0 0.4
Stopped going, mistreated 1 0.0 0.4
The —- Plan 1 0.0 0.4
The Healing Point Acupuncture and Wellness LLC 1 0.0 0.4
The Kauffman Clinic at Emory 1 0.0 0.4
Thibodand 1 0.0 0.4
Tricity health care 1 0.0 0.4
Tulane Hosp. 1 0.0 0.4
UCD Medical office 1 0.0 0.4
UCLA Health 1 0.0 0.4
UCLA Hospital 1 0.0 0.4
UCLA Medical Center. 1 0.0 0.4
UCSD MEDICAL CENTER 1 0.0 0.4
UCSF Medical Center. 1 0.0 0.4
UM Ann Arbor 1 0.0 0.4
University of Toledo Medical Center 1 0.0 0.4
Urgent Car-UT Robert B. Green. 1 0.0 0.4
urgent care 1 0.0 0.4
Urgent care 1 0.0 0.4
Urgent care at VA facility. 1 0.0 0.4
Urgent care facility 1 0.0 0.4
Urgent Care. 1 0.0 0.4
Urologist 1 0.0 0.4
Urologist specialist 1 0.0 0.4
Urologist/Diabetic Center/Hypertension doctor 1 0.0 0.4
Urology Specialist Clinic 1 0.0 0.4
UTHSC Da., TX. 1 0.0 0.4
UTSW Medical Center, Dallas, TX. Baylor-Scott White-Dallas, TX. Methodist Medical Center, Dallas, TX, Beckely Ave.. 1 0.0 0.4
Wayne State Urology 1 0.0 0.4
Wellness clinic-Ochsner 1 0.0 0.4
Wellstar Medical Center 1 0.0 0.4
Wellstar/Northside. 1 0.0 0.4
West Georgia Medical Center Villa —- GA 1 0.0 0.4
Willis Knighton —- Dr. Jennifer Kelly 1 0.0 0.4
Winn Army Community Hospital 1 0.0 0.4
Yearly physical exams 1 0.0 0.4
NA 5590 95.7 NA
Total 5839 100.0 100.0

C1: Years lived at current address

  • C1. How many years have you lived in your current address?
    • 1=Less than 1 year
    • 2=1-5 years
    • 3=6-10 years
    • 4=11-15 years
    • 5=16-20 years
    • 6=21+ years
  c1 <- as.factor(d[,"c1"])
# Make "*" to NA
c1[which(c1=="*")]<-"NA"
  levels(c1) <- list(Less_than_1_year="1",
                     years_1_5="2",
                     years_6_10="3",
                     years_11_15="4",
                     years_16_20="5",
                     years_21_more="6")
  c1 <- ordered(c1, c("Less_than_1_year", "years_1_5", "years_6_10", "years_11_15","years_16_20","years_21_more"))
  
  new.d <- data.frame(new.d, c1)
  new.d <- apply_labels(new.d, c1 = "living period")
  temp.d <- data.frame (new.d, c1)  
  
  result<-questionr::freq(temp.d$c1, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l')
n % val% %cum val%cum
Less_than_1_year 198 3.4 3.4 3.4 3.4
years_1_5 947 16.2 16.4 19.6 19.9
years_6_10 797 13.6 13.8 33.3 33.7
years_11_15 726 12.4 12.6 45.7 46.3
years_16_20 768 13.2 13.3 58.8 59.7
years_21_more 2321 39.7 40.3 98.6 100.0
NA 82 1.4 NA 100.0 NA
Total 5839 100.0 100.0 100.0 100.0

C2A: Feel safe walking in the neighborhood

    1. On average, I felt/feel safe walking in my neighborhood day or night.
      1. Current (from prostate cancer diagnosis to present)
      1. Age 31 up to just before prostate cancer diagnosis)
      1. Childhood or young adult life (up to age 30)
      • 1=Strongly Agree
      • 2=Agree
      • 3=Neutral (neither agree nor disagree)
      • 4=Disagree
      • 5=Strongly Disagree
  c2a1 <- as.factor(d[,"c2a1"])
# Make "*" to NA
c2a1[which(c2a1=="*")]<-"NA"
  levels(c2a1) <- list(Strongly_Agree="1",
                     Agree="2",
                     Neutral="3",
                     Disagree="4",
                     Strongly_Disagree="5")
  c2a1 <- ordered(c2a1, c("Strongly_Agree", "Agree", "Neutral", "Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, c2a1)
  new.d <- apply_labels(new.d, c2a1 = "walk in the neighborhood-current")
  temp.d <- data.frame (new.d, c2a1)  
  
  c2a2 <- as.factor(d[,"c2a2"])
  # Make "*" to NA
c2a2[which(c2a2=="*")]<-"NA"
  levels(c2a2) <- list(Strongly_Agree="1",
                     Agree="2",
                     Neutral="3",
                     Disagree="4",
                     Strongly_Disagree="5")
  c2a2 <- ordered(c2a2, c("Strongly_Agree", "Agree", "Neutral", "Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, c2a2)
  new.d <- apply_labels(new.d, c2a2 = "walk in the neighborhood-age 31 up")
  temp.d <- data.frame (new.d, c2a2) 
  
  c2a3 <- as.factor(d[,"c2a3"])
  # Make "*" to NA
c2a3[which(c2a3=="*")]<-"NA"
  levels(c2a3) <- list(Strongly_Agree="1",
                     Agree="2",
                     Neutral="3",
                     Disagree="4",
                     Strongly_Disagree="5")
  c2a3 <- ordered(c2a3, c("Strongly_Agree", "Agree", "Neutral", "Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, c2a3)
  new.d <- apply_labels(new.d, c2a3 = "walk in the neighborhood-Childhood or young")
  temp.d <- data.frame (new.d, c2a3)
  
  result<-questionr::freq(temp.d$c2a1, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "1. Current (from prostate cancer diagnosis to present)")
1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Strongly_Agree 2725 46.7 47.7 46.7 47.7
Agree 1941 33.2 33.9 79.9 81.6
Neutral 685 11.7 12.0 91.6 93.6
Disagree 282 4.8 4.9 96.5 98.5
Strongly_Disagree 85 1.5 1.5 97.9 100.0
NA 121 2.1 NA 100.0 NA
Total 5839 100.0 100.0 100.0 100.0 
  result<-questionr::freq(temp.d$c2a2, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "2. Age 31 up to just before prostate cancer diagnosis)")
2. Age 31 up to just before prostate cancer diagnosis)
n % val% %cum val%cum
Strongly_Agree 2317 39.7 42.3 39.7 42.3
Agree 1970 33.7 35.9 73.4 78.2
Neutral 834 14.3 15.2 87.7 93.4
Disagree 276 4.7 5.0 92.4 98.5
Strongly_Disagree 84 1.4 1.5 93.9 100.0
NA 358 6.1 NA 100.0 NA
Total 5839 100.0 100.0 100.0 100.0 
  result<-questionr::freq(temp.d$c2a3, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Strongly_Agree 2422 41.5 44.7 41.5 44.7
Agree 1698 29.1 31.3 70.6 76.0
Neutral 838 14.4 15.5 84.9 91.5
Disagree 348 6.0 6.4 90.9 97.9
Strongly_Disagree 114 2.0 2.1 92.8 100.0
NA 419 7.2 NA 100.0 NA
Total 5839 100.0 100.0 100.0 100.0

C2B: Violence

    1. Violence was/is not a problem in my neighborhood.
      1. Current (from prostate cancer diagnosis to present)
      1. Age 31 up to just before prostate cancer diagnosis)
      1. Childhood or young adult life (up to age 30)
      • 1=Strongly Agree
      • 2=Agree
      • 3=Neutral (neither agree nor disagree)
      • 4=Disagree
      • 5=Strongly Disagree
  c2b1 <- as.factor(d[,"c2b1"])
# Make "*" to NA
c2b1[which(c2b1=="*")]<-"NA"
  levels(c2b1) <- list(Strongly_Agree="1",
                     Agree="2",
                     Neutral="3",
                     Disagree="4",
                     Strongly_Disagree="5")
  c2b1 <- ordered(c2b1, c("Strongly_Agree", "Agree", "Neutral", "Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, c2b1)
  new.d <- apply_labels(new.d, c2b1 = "Violence in the neighborhood-current")
  temp.d <- data.frame (new.d, c2b1)  
  
  c2b2 <- as.factor(d[,"c2b2"])
  # Make "*" to NA
c2b2[which(c2b2=="*")]<-"NA"
  levels(c2b2) <- list(Strongly_Agree="1",
                     Agree="2",
                     Neutral="3",
                     Disagree="4",
                     Strongly_Disagree="5")
  c2b2 <- ordered(c2b2, c("Strongly_Agree", "Agree", "Neutral", "Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, c2b2)
  new.d <- apply_labels(new.d, c2b2 = "Violence in the neighborhood-age 31 up")
  temp.d <- data.frame (new.d, c2b2) 
  
  c2b3 <- as.factor(d[,"c2b3"])
  # Make "*" to NA
c2b3[which(c2b3=="*")]<-"NA"
  levels(c2b3) <- list(Strongly_Agree="1",
                     Agree="2",
                     Neutral="3",
                     Disagree="4",
                     Strongly_Disagree="5")
  c2b3 <- ordered(c2b3, c("Strongly_Agree", "Agree", "Neutral", "Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, c2b3)
  new.d <- apply_labels(new.d, c2b3 = "Violence in the neighborhood-Childhood or young")
  temp.d <- data.frame (new.d, c2b3)
  
  result<-questionr::freq(temp.d$c2b1, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "1. Current (from prostate cancer diagnosis to present)")
1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Strongly_Agree 2336 40.0 41.1 40.0 41.1
Agree 1807 30.9 31.8 71.0 73.0
Neutral 841 14.4 14.8 85.4 87.8
Disagree 485 8.3 8.5 93.7 96.3
Strongly_Disagree 208 3.6 3.7 97.2 100.0
NA 162 2.8 NA 100.0 NA
Total 5839 100.0 100.0 100.0 100.0 
  result<-questionr::freq(temp.d$c2b2, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "2. Age 31 up to just before prostate cancer diagnosis)")
2. Age 31 up to just before prostate cancer diagnosis)
n % val% %cum val%cum
Strongly_Agree 1881 32.2 34.6 32.2 34.6
Agree 1858 31.8 34.1 64.0 68.7
Neutral 1011 17.3 18.6 81.3 87.3
Disagree 511 8.8 9.4 90.1 96.6
Strongly_Disagree 183 3.1 3.4 93.2 100.0
NA 395 6.8 NA 100.0 NA
Total 5839 100.0 100.0 100.0 100.0 
  result<-questionr::freq(temp.d$c2b3, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Strongly_Agree 1854 31.8 34.3 31.8 34.3
Agree 1689 28.9 31.2 60.7 65.5
Neutral 1018 17.4 18.8 78.1 84.4
Disagree 614 10.5 11.4 88.6 95.7
Strongly_Disagree 231 4.0 4.3 92.6 100.0
NA 433 7.4 NA 100.0 NA
Total 5839 100.0 100.0 100.0 100.0

C2C: Safe from crime

    1. My neighborhood was/is safe from crime.
      1. Current (from prostate cancer diagnosis to present)
      1. Age 31 up to just before prostate cancer diagnosis)
      1. Childhood or young adult life (up to age 30)
      • 1=Strongly Agree
      • 2=Agree
      • 3=Neutral (neither agree nor disagree)
      • 4=Disagree
      • 5=Strongly Disagree
  c2c1 <- as.factor(d[,"c2c1"])
# Make "*" to NA
c2c1[which(c2c1=="*")]<-"NA"
  levels(c2c1) <- list(Strongly_Agree="1",
                     Agree="2",
                     Neutral="3",
                     Disagree="4",
                     Strongly_Disagree="5")
  c2c1 <- ordered(c2c1, c("Strongly_Agree", "Agree", "Neutral", "Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, c2c1)
  new.d <- apply_labels(new.d, c2c1 = "safe from crime in the neighborhood-current")
  temp.d <- data.frame (new.d, c2c1)  
  
  c2c2 <- as.factor(d[,"c2c2"])
  # Make "*" to NA
c2c2[which(c2c2=="*")]<-"NA"
  levels(c2c2) <- list(Strongly_Agree="1",
                     Agree="2",
                     Neutral="3",
                     Disagree="4",
                     Strongly_Disagree="5")
  c2c2 <- ordered(c2c2, c("Strongly_Agree", "Agree", "Neutral", "Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, c2c2)
  new.d <- apply_labels(new.d, c2c2 = "safe from crime in the neighborhood-age 31 up")
  temp.d <- data.frame (new.d, c2c2) 
  
  c2c3 <- as.factor(d[,"c2c3"])
  # Make "*" to NA
c2c3[which(c2c3=="*")]<-"NA"
  levels(c2c3) <- list(Strongly_Agree="1",
                     Agree="2",
                     Neutral="3",
                     Disagree="4",
                     Strongly_Disagree="5")
  c2c3 <- ordered(c2c3, c("Strongly_Agree", "Agree", "Neutral", "Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, c2c3)
  new.d <- apply_labels(new.d, c2c3 = "safe from crime in the neighborhood-Childhood or young")
  temp.d <- data.frame (new.d, c2c3)
  
  result<-questionr::freq(temp.d$c2c1, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "1. Current (from prostate cancer diagnosis to present)")
1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Strongly_Agree 1706 29.2 30.2 29.2 30.2
Agree 1800 30.8 31.8 60.0 62.0
Neutral 1218 20.9 21.5 80.9 83.6
Disagree 729 12.5 12.9 93.4 96.4
Strongly_Disagree 201 3.4 3.6 96.8 100.0
NA 185 3.2 NA 100.0 NA
Total 5839 100.0 100.0 100.0 100.0 
  result<-questionr::freq(temp.d$c2c2, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "2. Age 31 up to just before prostate cancer diagnosis)")
2. Age 31 up to just before prostate cancer diagnosis)
n % val% %cum val%cum
Strongly_Agree 1424 24.4 26.4 24.4 26.4
Agree 1828 31.3 33.9 55.7 60.2
Neutral 1257 21.5 23.3 77.2 83.5
Disagree 722 12.4 13.4 89.6 96.9
Strongly_Disagree 168 2.9 3.1 92.5 100.0
NA 440 7.5 NA 100.0 NA
Total 5839 100.0 100.0 100.0 100.0 
  result<-questionr::freq(temp.d$c2c3, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Strongly_Agree 1508 25.8 28.0 25.8 28.0
Agree 1636 28.0 30.4 53.8 58.4
Neutral 1250 21.4 23.2 75.3 81.7
Disagree 743 12.7 13.8 88.0 95.5
Strongly_Disagree 243 4.2 4.5 92.1 100.0
NA 459 7.9 NA 100.0 NA
Total 5839 100.0 100.0 100.0 100.0

C3A: Traffic

  • C3. Thinking about your neighborhood during the following 3 time periods, as a whole, how much of a problem is/was…
    1. Traffic
      1. Current (from prostate cancer diagnosis to present)
      1. Age 31 up to just before prostate cancer diagnosis
      1. Childhood or young adult life (up to age 30)
      • 1=Non/Minor problem
      • 2=Somewhat serious problem
      • 3=Very serious problem
      • 88=Don’t Know
  c3a1 <- as.factor(d[,"c3a1"])
# Make "*" to NA
c3a1[which(c3a1=="*")]<-"NA"
  levels(c3a1) <- list(Non_Minor="1",
                     Somewhat_serious="2",
                     Very_serious="3",
                     Dont_know="88")
  c3a1 <- ordered(c3a1, c("Non_Minor", "Somewhat_serious", "Very_serious", "Dont_know"))
  
  new.d <- data.frame(new.d, c3a1)
  new.d <- apply_labels(new.d, c3a1 = "A lot of noise-Current")
  temp.d <- data.frame (new.d, c3a1)  
  
  c3a2 <- as.factor(d[,"c3a2"])
  # Make "*" to NA
c3a2[which(c3a2=="*")]<-"NA"
  levels(c3a2) <- list(Non_Minor="1",
                     Somewhat_serious="2",
                     Very_serious="3",
                     Dont_know="88")
  c3a2 <- ordered(c3a2, c("Non_Minor", "Somewhat_serious", "Very_serious", "Dont_know"))
  
  new.d <- data.frame(new.d, c3a2)
  new.d <- apply_labels(new.d, c3a2 = "A lot of noise-age 31 up")
  temp.d <- data.frame (new.d, c3a2) 
  
  c3a3 <- as.factor(d[,"c3a3"])
  # Make "*" to NA
c3a3[which(c3a3=="*")]<-"NA"
  levels(c3a3) <- list(Non_Minor="1",
                     Somewhat_serious="2",
                     Very_serious="3",
                     Dont_know="88")
  c3a3 <- ordered(c3a3, c("Non_Minor", "Somewhat_serious", "Very_serious", "Dont_know"))
  
  new.d <- data.frame(new.d, c3a3)
  new.d <- apply_labels(new.d, c3a3 = "A lot of noise-Childhood or young")
  temp.d <- data.frame (new.d, c3a3)
  
  result<-questionr::freq(temp.d$c3a1, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "1. Current (from prostate cancer diagnosis to present)")
1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Non_Minor 4029 69.0 70.8 69.0 70.8
Somewhat_serious 1049 18.0 18.4 87.0 89.2
Very_serious 383 6.6 6.7 93.5 95.9
Dont_know 232 4.0 4.1 97.5 100.0
NA 146 2.5 NA 100.0 NA
Total 5839 100.0 100.0 100.0 100.0 
  result<-questionr::freq(temp.d$c3a2, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val% %cum val%cum
Non_Minor 3805 65.2 69.1 65.2 69.1
Somewhat_serious 1178 20.2 21.4 85.3 90.5
Very_serious 238 4.1 4.3 89.4 94.8
Dont_know 288 4.9 5.2 94.3 100.0
NA 330 5.7 NA 100.0 NA
Total 5839 100.0 100.0 100.0 100.0 
  result<-questionr::freq(temp.d$c3a3, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Non_Minor 4194 71.8 76.7 71.8 76.7
Somewhat_serious 689 11.8 12.6 83.6 89.3
Very_serious 157 2.7 2.9 86.3 92.2
Dont_know 427 7.3 7.8 93.6 100.0
NA 372 6.4 NA 100.0 NA
Total 5839 100.0 100.0 100.0 100.0

C3B: Noise

  • C3. Thinking about your neighborhood during the following 3 time periods, as a whole, how much of a problem is/was…
    1. A lot of noise
      1. Current (from prostate cancer diagnosis to present)
      1. Age 31 up to just before prostate cancer diagnosis
      1. Childhood or young adult life (up to age 30)
      • 1=Non/Minor problem
      • 2=Somewhat serious problem
      • 3=Very serious problem
      • 88=Don’t Know
  c3b1 <- as.factor(d[,"c3b1"])
# Make "*" to NA
c3b1[which(c3b1=="*")]<-"NA"
  levels(c3b1) <- list(Non_Minor="1",
                     Somewhat_serious="2",
                     Very_serious="3",
                     Dont_know="88")
  c3b1 <- ordered(c3b1, c("Non_Minor", "Somewhat_serious", "Very_serious", "Dont_know"))
  
  new.d <- data.frame(new.d, c3b1)
  new.d <- apply_labels(new.d, c3b1 = "A lot of noise-Current")
  temp.d <- data.frame (new.d, c3b1)  
  
  c3b2 <- as.factor(d[,"c3b2"])
  # Make "*" to NA
c3b2[which(c3b2=="*")]<-"NA"
  levels(c3b2) <- list(Non_Minor="1",
                     Somewhat_serious="2",
                     Very_serious="3",
                     Dont_know="88")
  c3b2 <- ordered(c3b2, c("Non_Minor", "Somewhat_serious", "Very_serious", "Dont_know"))
  
  new.d <- data.frame(new.d, c3b2)
  new.d <- apply_labels(new.d, c3b2 = "A lot of noise-age 31 up")
  temp.d <- data.frame (new.d, c3b2) 
  
  c3b3 <- as.factor(d[,"c3b3"])
  # Make "*" to NA
c3b3[which(c3b3=="*")]<-"NA"
  levels(c3b3) <- list(Non_Minor="1",
                     Somewhat_serious="2",
                     Very_serious="3",
                     Dont_know="88")
  c3b3 <- ordered(c3b3, c("Non_Minor", "Somewhat_serious", "Very_serious", "Dont_know"))
  
  new.d <- data.frame(new.d, c3b3)
  new.d <- apply_labels(new.d, c3b3 = "A lot of noise-Childhood or young")
  temp.d <- data.frame (new.d, c3b3)
  
  result<-questionr::freq(temp.d$c3b1, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "1. Current (from prostate cancer diagnosis to present)")
1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Non_Minor 4637 79.4 82.0 79.4 82.0
Somewhat_serious 731 12.5 12.9 91.9 94.9
Very_serious 159 2.7 2.8 94.7 97.7
Dont_know 130 2.2 2.3 96.9 100.0
NA 182 3.1 NA 100.0 NA
Total 5839 100.0 100.0 100.0 100.0 
  result<-questionr::freq(temp.d$c3b2, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val% %cum val%cum
Non_Minor 4217 72.2 77.2 72.2 77.2
Somewhat_serious 882 15.1 16.1 87.3 93.3
Very_serious 169 2.9 3.1 90.2 96.4
Dont_know 195 3.3 3.6 93.6 100.0
NA 376 6.4 NA 100.0 NA
Total 5839 100.0 100.0 100.0 100.0 
  result<-questionr::freq(temp.d$c3b3, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Non_Minor 4185 71.7 77.0 71.7 77.0
Somewhat_serious 741 12.7 13.6 84.4 90.7
Very_serious 181 3.1 3.3 87.5 94.0
Dont_know 325 5.6 6.0 93.0 100.0
NA 407 7.0 NA 100.0 NA
Total 5839 100.0 100.0 100.0 100.0

C3C: Trash and litter

  • C3. Thinking about your neighborhood during the following 3 time periods, as a whole, how much of a problem is/was…
    1. Trash and litter
      1. Current (from prostate cancer diagnosis to present)
      1. Age 31 up to just before prostate cancer diagnosis
      1. Childhood or young adult life (up to age 30)
      • 1=Non/Minor problem
      • 2=Somewhat serious problem
      • 3=Very serious problem
      • 88=Don’t Know
  c3c1 <- as.factor(d[,"c3c1"])
# Make "*" to NA
c3c1[which(c3c1=="*")]<-"NA"
  levels(c3c1) <- list(Non_Minor="1",
                     Somewhat_serious="2",
                     Very_serious="3",
                     Dont_know="88")
  c3c1 <- ordered(c3c1, c("Non_Minor", "Somewhat_serious", "Very_serious", "Dont_know"))
  
  new.d <- data.frame(new.d, c3c1)
  new.d <- apply_labels(new.d, c3c1 = "Trash and litter-Current")
  temp.d <- data.frame (new.d, c3c1)  
  
  c3c2 <- as.factor(d[,"c3c2"])
  # Make "*" to NA
c3c2[which(c3c2=="*")]<-"NA"
  levels(c3c2) <- list(Non_Minor="1",
                     Somewhat_serious="2",
                     Very_serious="3",
                     Dont_know="88")
  c3c2 <- ordered(c3c2, c("Non_Minor", "Somewhat_serious", "Very_serious", "Dont_know"))
  
  new.d <- data.frame(new.d, c3c2)
  new.d <- apply_labels(new.d, c3c2 = "Trash and litter-age 31 up")
  temp.d <- data.frame (new.d, c3c2) 
  
  c3c3 <- as.factor(d[,"c3c3"])
  # Make "*" to NA
c3c3[which(c3c3=="*")]<-"NA"
  levels(c3c3) <- list(Non_Minor="1",
                     Somewhat_serious="2",
                     Very_serious="3",
                     Dont_know="88")
  c3c3 <- ordered(c3c3, c("Non_Minor", "Somewhat_serious", "Very_serious", "Dont_know"))
  
  new.d <- data.frame(new.d, c3c3)
  new.d <- apply_labels(new.d, c3c3 = "Trash and litter-Childhood or young")
  temp.d <- data.frame (new.d, c3c3)
  
  result<-questionr::freq(temp.d$c3c1, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "1. Current (from prostate cancer diagnosis to present)")
1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Non_Minor 4690 80.3 83.0 80.3 83.0
Somewhat_serious 612 10.5 10.8 90.8 93.8
Very_serious 245 4.2 4.3 95.0 98.1
Dont_know 107 1.8 1.9 96.8 100.0
NA 185 3.2 NA 100.0 NA
Total 5839 100.0 100.0 100.0 100.0 
  result<-questionr::freq(temp.d$c3c2, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val% %cum val%cum
Non_Minor 4359 74.7 79.8 74.7 79.8
Somewhat_serious 754 12.9 13.8 87.6 93.6
Very_serious 178 3.0 3.3 90.6 96.9
Dont_know 172 2.9 3.1 93.6 100.0
NA 376 6.4 NA 100.0 NA
Total 5839 100.0 100.0 100.0 100.0 
  result<-questionr::freq(temp.d$c3c3, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Non_Minor 4223 72.3 77.7 72.3 77.7
Somewhat_serious 726 12.4 13.4 84.8 91.1
Very_serious 207 3.5 3.8 88.3 94.9
Dont_know 278 4.8 5.1 93.1 100.0
NA 405 6.9 NA 100.0 NA
Total 5839 100.0 100.0 100.0 100.0

C3D: Too much light at night

  • C3. Thinking about your neighborhood during the following 3 time periods, as a whole, how much of a problem is/was…
    1. Too much light at night
      1. Current (from prostate cancer diagnosis to present)
      1. Age 31 up to just before prostate cancer diagnosis
      1. Childhood or young adult life (up to age 30)
      • 1=Non/Minor problem
      • 2=Somewhat serious problem
      • 3=Very serious problem
      • 88=Don’t Know
  c3d1 <- as.factor(d[,"c3d1"])
# Make "*" to NA
c3d1[which(c3d1=="*")]<-"NA"
  levels(c3d1) <- list(Non_Minor="1",
                     Somewhat_serious="2",
                     Very_serious="3",
                     Dont_know="88")
  c3d1 <- ordered(c3d1, c("Non_Minor", "Somewhat_serious", "Very_serious", "Dont_know"))
  
  new.d <- data.frame(new.d, c3d1)
  new.d <- apply_labels(new.d, c3d1 = "Too much light at night-Current")
  temp.d <- data.frame (new.d, c3d1)  
  
  c3d2 <- as.factor(d[,"c3d2"])
  # Make "*" to NA
c3d2[which(c3d2=="*")]<-"NA"
  levels(c3d2) <- list(Non_Minor="1",
                     Somewhat_serious="2",
                     Very_serious="3",
                     Dont_know="88")
  c3d2 <- ordered(c3d2, c("Non_Minor", "Somewhat_serious", "Very_serious", "Dont_know"))
  
  new.d <- data.frame(new.d, c3d2)
  new.d <- apply_labels(new.d, c3d2 = "Too much light at night-age 31 up")
  temp.d <- data.frame (new.d, c3d2) 
  
  c3d3 <- as.factor(d[,"c3d3"])
  # Make "*" to NA
c3d3[which(c3d3=="*")]<-"NA"
  levels(c3d3) <- list(Non_Minor="1",
                     Somewhat_serious="2",
                     Very_serious="3",
                     Dont_know="88")
  c3d3 <- ordered(c3d3, c("Non_Minor", "Somewhat_serious", "Very_serious", "Dont_know"))
  
  new.d <- data.frame(new.d, c3d3)
  new.d <- apply_labels(new.d, c3d3 = "Too much light at night-Childhood or young")
  temp.d <- data.frame (new.d, c3d3)
  
  result<-questionr::freq(temp.d$c3d1, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "1. Current (from prostate cancer diagnosis to present)")
1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Non_Minor 5208 89.2 92.2 89.2 92.2
Somewhat_serious 181 3.1 3.2 92.3 95.4
Very_serious 49 0.8 0.9 93.1 96.3
Dont_know 208 3.6 3.7 96.7 100.0
NA 193 3.3 NA 100.0 NA
Total 5839 100.0 100.0 100.0 100.0 
  result<-questionr::freq(temp.d$c3d2, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val% %cum val%cum
Non_Minor 4880 83.6 89.5 83.6 89.5
Somewhat_serious 280 4.8 5.1 88.4 94.6
Very_serious 48 0.8 0.9 89.2 95.5
Dont_know 245 4.2 4.5 93.4 100.0
NA 386 6.6 NA 100.0 NA
Total 5839 100.0 100.0 100.0 100.0 
  result<-questionr::freq(temp.d$c3d3, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Non_Minor 4750 81.3 87.3 81.3 87.3
Somewhat_serious 252 4.3 4.6 85.7 92.0
Very_serious 53 0.9 1.0 86.6 92.9
Dont_know 384 6.6 7.1 93.1 100.0
NA 400 6.9 NA 100.0 NA
Total 5839 100.0 100.0 100.0 100.0

C4A: Neighbors talking outside

  • C4. Thinking about your NEIGHBORS, as a whole, during the following 3 time periods:
    1. How often do/did you see neighbors talking outside in the yard, on the street, at the corner park, etc.?
      1. Current (from prostate cancer diagnosis to present)
      1. Age 31 up to just before prostate cancer diagnosis
      1. Childhood or young adult life (up to age 30)
      • 1=Often
      • 2=Sometimes
      • 3=Rarely/Never
      • 88=Don’t Know
  c4a1 <- as.factor(d[,"c4a1"])
# Make "*" to NA
c4a1[which(c4a1=="*")]<-"NA"
  levels(c4a1) <- list(Often="1",
                     Sometimes="2",
                     Rarely_Never="3",
                     Dont_know="88")
  c4a1 <- ordered(c4a1, c("Often", "Sometimes", "Rarely_Never", "Dont_know"))
  
  new.d <- data.frame(new.d, c4a1)
  new.d <- apply_labels(new.d, c4a1 = "Talk outside-Current")
  temp.d <- data.frame (new.d, c4a1)  
  
  c4a2 <- as.factor(d[,"c4a2"])
# Make "*" to NA
c4a2[which(c4a2=="*")]<-"NA" 
  levels(c4a2) <- list(Often="1",
                     Sometimes="2",
                     Rarely_Never="3",
                     Dont_know="88")
  c4a2 <- ordered(c4a2, c("Often", "Sometimes", "Rarely_Never", "Dont_know"))
  
  new.d <- data.frame(new.d, c4a2)
  new.d <- apply_labels(new.d, c4a2 = "Talk outside-age 31 up")
  temp.d <- data.frame (new.d, c4a2) 
  
  c4a3 <- as.factor(d[,"c4a3"])
  # Make "*" to NA
c4a3[which(c4a3=="*")]<-"NA"
  levels(c4a3) <- list(Often="1",
                     Sometimes="2",
                     Rarely_Never="3",
                     Dont_know="88")
  c4a3 <- ordered(c4a3, c("Often", "Sometimes", "Rarely_Never", "Dont_know"))
  
  new.d <- data.frame(new.d, c4a3)
  new.d <- apply_labels(new.d, c4a3 = "Talk outside-Childhood or young")
  temp.d <- data.frame (new.d, c4a3)
  
  result<-questionr::freq(temp.d$c4a1, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "1. Current (from prostate cancer diagnosis to present)")
1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Often 2131 36.5 37.3 36.5 37.3
Sometimes 2391 40.9 41.9 77.4 79.2
Rarely_Never 1095 18.8 19.2 96.2 98.4
Dont_know 92 1.6 1.6 97.8 100.0
NA 130 2.2 NA 100.0 NA
Total 5839 100.0 100.0 100.0 100.0 
  result<-questionr::freq(temp.d$c4a2, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val% %cum val%cum
Often 2125 36.4 38.6 36.4 38.6
Sometimes 2422 41.5 43.9 77.9 82.5
Rarely_Never 776 13.3 14.1 91.2 96.6
Dont_know 188 3.2 3.4 94.4 100.0
NA 328 5.6 NA 100.0 NA
Total 5839 100.0 100.0 100.0 100.0 
  result<-questionr::freq(temp.d$c4a3, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Often 3132 53.6 57.4 53.6 57.4
Sometimes 1508 25.8 27.6 79.5 85.0
Rarely_Never 504 8.6 9.2 88.1 94.2
Dont_know 316 5.4 5.8 93.5 100.0
NA 379 6.5 NA 100.0 NA
Total 5839 100.0 100.0 100.0 100.0

C4B: Neighbors watch out for each other

  • C4. Thinking about your NEIGHBORS, as a whole, during the following 3 time periods:
    1. How often do/did neighbors watch out for each other, such as calling if they see a problem?
      1. Current (from prostate cancer diagnosis to present)
      1. Age 31 up to just before prostate cancer diagnosis
      1. Childhood or young adult life (up to age 30)
      • 1=Often
      • 2=Sometimes
      • 3=Rarely/Never
      • 88=Don’t Know
  c4b1 <- as.factor(d[,"c4b1"])
# Make "*" to NA
c4b1[which(c4b1=="*")]<-"NA"
  levels(c4b1) <- list(Often="1",
                     Sometimes="2",
                     Rarely_Never="3",
                     Dont_know="88")
  c4b1 <- ordered(c4b1, c("Often", "Sometimes", "Rarely_Never", "Dont_know"))
  
  new.d <- data.frame(new.d, c4b1)
  new.d <- apply_labels(new.d, c4b1 = "watch out-Current")
  temp.d <- data.frame (new.d, c4b1)  
  
  c4b2 <- as.factor(d[,"c4b2"])
  # Make "*" to NA
c4b2[which(c4b2=="*")]<-"NA"
  levels(c4b2) <- list(Often="1",
                     Sometimes="2",
                     Rarely_Never="3",
                     Dont_know="88")
  c4b2 <- ordered(c4b2, c("Often", "Sometimes", "Rarely_Never", "Dont_know"))
  
  new.d <- data.frame(new.d, c4b2)
  new.d <- apply_labels(new.d, c4b2 = "watch out-age 31 up")
  temp.d <- data.frame (new.d, c4b2) 
  
  c4b3 <- as.factor(d[,"c4b3"])
  # Make "*" to NA
c4b3[which(c4b3=="*")]<-"NA"
  levels(c4b3) <- list(Often="1",
                     Sometimes="2",
                     Rarely_Never="3",
                     Dont_know="88")
  c4b3 <- ordered(c4b3, c("Often", "Sometimes", "Rarely_Never", "Dont_know"))
  
  new.d <- data.frame(new.d, c4b3)
  new.d <- apply_labels(new.d, c4b3 = "watch out-Childhood or young")
  temp.d <- data.frame (new.d, c4b3)
  
  result<-questionr::freq(temp.d$c4b1, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "1. Current (from prostate cancer diagnosis to present)")
1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Often 2622 44.9 46.6 44.9 46.6
Sometimes 1770 30.3 31.5 75.2 78.1
Rarely_Never 868 14.9 15.4 90.1 93.6
Dont_know 362 6.2 6.4 96.3 100.0
NA 217 3.7 NA 100.0 NA
Total 5839 100.0 100.0 100.0 100.0 
  result<-questionr::freq(temp.d$c4b2, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val% %cum val%cum
Often 2268 38.8 41.8 38.8 41.8
Sometimes 1984 34.0 36.6 72.8 78.4
Rarely_Never 816 14.0 15.0 86.8 93.5
Dont_know 354 6.1 6.5 92.9 100.0
NA 417 7.1 NA 100.0 NA
Total 5839 100.0 100.0 100.0 100.0 
  result<-questionr::freq(temp.d$c4b3, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Often 3119 53.4 58.0 53.4 58.0
Sometimes 1272 21.8 23.6 75.2 81.6
Rarely_Never 522 8.9 9.7 84.1 91.3
Dont_know 466 8.0 8.7 92.1 100.0
NA 460 7.9 NA 100.0 NA
Total 5839 100.0 100.0 100.0 100.0

C4C: Neighbors know by name

  • C4. Thinking about your NEIGHBORS, as a whole, during the following 3 time periods:
    1. How many neighbors do/did you know by name?
      1. Current (from prostate cancer diagnosis to present)
      1. Age 31 up to just before prostate cancer diagnosis
      1. Childhood or young adult life (up to age 30)
      • 1=Often
      • 2=Sometimes
      • 3=Rarely/Never
      • 88=Don’t Know
  c4c1 <- as.factor(d[,"c4c1"])
# Make "*" to NA
c4c1[which(c4c1=="*")]<-"NA"
  levels(c4c1) <- list(Often="1",
                     Sometimes="2",
                     Rarely_Never="3",
                     Dont_know="88")
  c4c1 <- ordered(c4c1, c("Often", "Sometimes", "Rarely_Never", "Dont_know"))
  
  new.d <- data.frame(new.d, c4c1)
  new.d <- apply_labels(new.d, c4c1 = "Know names-Current")
  temp.d <- data.frame (new.d, c4c1)  
  
  c4c2 <- as.factor(d[,"c4c2"])
# Make "*" to NA
c4c2[which(c4c2=="*")]<-"NA"
  levels(c4c2) <- list(Often="1",
                     Sometimes="2",
                     Rarely_Never="3",
                     Dont_know="88")
  c4c2 <- ordered(c4c2, c("Often", "Sometimes", "Rarely_Never", "Dont_know"))
  
  new.d <- data.frame(new.d, c4c2)
  new.d <- apply_labels(new.d, c4c2 = "Know names-age 31 up")
  temp.d <- data.frame (new.d, c4c2) 
  
  c4c3 <- as.factor(d[,"c4c3"])
# Make "*" to NA
c4c3[which(c4c3=="*")]<-"NA"
  levels(c4c3) <- list(Often="1",
                     Sometimes="2",
                     Rarely_Never="3",
                     Dont_know="88")
  c4c3 <- ordered(c4c3, c("Often", "Sometimes", "Rarely_Never", "Dont_know"))
  
  new.d <- data.frame(new.d, c4c3)
  new.d <- apply_labels(new.d, c4c3 = "Know names-Childhood or young")
  temp.d <- data.frame (new.d, c4c3)
  
  result<-questionr::freq(temp.d$c4c1, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "1. Current (from prostate cancer diagnosis to present)")
1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Often 1630 27.9 29.2 27.9 29.2
Sometimes 2338 40.0 41.8 68.0 71.0
Rarely_Never 1533 26.3 27.4 94.2 98.4
Dont_know 87 1.5 1.6 95.7 100.0
NA 251 4.3 NA 100.0 NA
Total 5839 100.0 100.0 100.0 100.0 
  result<-questionr::freq(temp.d$c4c2, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val% %cum val%cum
Often 1709 29.3 31.9 29.3 31.9
Sometimes 2376 40.7 44.3 70.0 76.2
Rarely_Never 1133 19.4 21.1 89.4 97.3
Dont_know 146 2.5 2.7 91.9 100.0
NA 475 8.1 NA 100.0 NA
Total 5839 100.0 100.0 100.0 100.0 
  result<-questionr::freq(temp.d$c4c3, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Often 3198 54.8 60.1 54.8 60.1
Sometimes 1294 22.2 24.3 76.9 84.4
Rarely_Never 596 10.2 11.2 87.1 95.5
Dont_know 237 4.1 4.5 91.2 100.0
NA 514 8.8 NA 100.0 NA
Total 5839 100.0 100.0 100.0 100.0

C4D: Friendly talks with neighbors

  • C4. Thinking about your NEIGHBORS, as a whole, during the following 3 time periods:
    1. How many neighbors do/did you have a friendly talk with at least once a week?
      1. Current (from prostate cancer diagnosis to present)
      1. Age 31 up to just before prostate cancer diagnosis
      1. Childhood or young adult life (up to age 30)
      • 1=Often
      • 2=Sometimes
      • 3=Rarely/Never
      • 88=Don’t Know
  c4d1 <- as.factor(d[,"c4d1"])
# Make "*" to NA
c4d1[which(c4d1=="*")]<-"NA"
  levels(c4d1) <- list(Often="1",
                     Sometimes="2",
                     Rarely_Never="3",
                     Dont_know="88")
  c4d1 <- ordered(c4d1, c("Often", "Sometimes", "Rarely_Never", "Dont_know"))
  
  new.d <- data.frame(new.d, c4d1)
  new.d <- apply_labels(new.d, c4d1 = "Know names-Current")
  temp.d <- data.frame (new.d, c4d1)  
  
  c4d2 <- as.factor(d[,"c4d2"])
# Make "*" to NA
c4d2[which(c4d2=="*")]<-"NA"
  levels(c4d2) <- list(Often="1",
                     Sometimes="2",
                     Rarely_Never="3",
                     Dont_know="88")
  c4d2 <- ordered(c4d2, c("Often", "Sometimes", "Rarely_Never", "Dont_know"))
  
  new.d <- data.frame(new.d, c4d2)
  new.d <- apply_labels(new.d, c4d2 = "Know names-age 31 up")
  temp.d <- data.frame (new.d, c4d2) 
  
  c4d3 <- as.factor(d[,"c4d3"])
  # Make "*" to NA
c4d3[which(c4d3=="*")]<-"NA"
  levels(c4d3) <- list(Often="1",
                     Sometimes="2",
                     Rarely_Never="3",
                     Dont_know="88")
  c4d3 <- ordered(c4d3, c("Often", "Sometimes", "Rarely_Never", "Dont_know"))
  
  new.d <- data.frame(new.d, c4d3)
  new.d <- apply_labels(new.d, c4d3 = "Know names-Childhood or young")
  temp.d <- data.frame (new.d, c4d3)
  
  result<-questionr::freq(temp.d$c4d1, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "1. Current (from prostate cancer diagnosis to present)")
1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Often 759 13.0 13.5 13.0 13.5
Sometimes 2095 35.9 37.3 48.9 50.8
Rarely_Never 2656 45.5 47.3 94.4 98.2
Dont_know 103 1.8 1.8 96.1 100.0
NA 226 3.9 NA 100.0 NA
Total 5839 100.0 100.0 100.0 100.0 
  result<-questionr::freq(temp.d$c4d2, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val% %cum val%cum
Often 939 16.1 17.4 16.1 17.4
Sometimes 2387 40.9 44.2 57.0 61.6
Rarely_Never 1895 32.5 35.1 89.4 96.7
Dont_know 176 3.0 3.3 92.4 100.0
NA 442 7.6 NA 100.0 NA
Total 5839 100.0 100.0 100.0 100.0 
  result<-questionr::freq(temp.d$c4d3, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Often 2310 39.6 43.0 39.6 43.0
Sometimes 1681 28.8 31.3 68.4 74.3
Rarely_Never 1071 18.3 19.9 86.7 94.2
Dont_know 309 5.3 5.8 92.0 100.0
NA 468 8.0 NA 100.0 NA
Total 5839 100.0 100.0 100.0 100.0

C4E: Ask neighbors for help

  • C4. Thinking about your NEIGHBORS, as a whole, during the following 3 time periods:
    1. How many neighbors could you ask for help, such as to “borrow a cup of sugar” or some other small favor?
      1. Current (from prostate cancer diagnosis to present)
      1. Age 31 up to just before prostate cancer diagnosis
      1. Childhood or young adult life (up to age 30)
      • 1=Often
      • 2=Sometimes
      • 3=Rarely/Never
      • 88=Don’t Know
  c4e1 <- as.factor(d[,"c4e1"])
# Make "*" to NA
c4e1[which(c4e1=="*")]<-"NA"
  levels(c4e1) <- list(Often="1",
                     Sometimes="2",
                     Rarely_Never="3",
                     Dont_know="88")
  c4e1 <- ordered(c4e1, c("Often", "Sometimes", "Rarely_Never", "Dont_know"))
  
  new.d <- data.frame(new.d, c4e1)
  new.d <- apply_labels(new.d, c4e1 = "ask for help-Current")
  temp.d <- data.frame (new.d, c4e1)  
  
  c4e2 <- as.factor(d[,"c4e2"])
# Make "*" to NA
c4e2[which(c4e2=="*")]<-"NA"
  levels(c4e2) <- list(Often="1",
                     Sometimes="2",
                     Rarely_Never="3",
                     Dont_know="88")
  c4e2 <- ordered(c4e2, c("Often", "Sometimes", "Rarely_Never", "Dont_know"))
  
  new.d <- data.frame(new.d, c4e2)
  new.d <- apply_labels(new.d, c4e2 = "ask for help-age 31 up")
  temp.d <- data.frame (new.d, c4e2) 
  
  c4e3 <- as.factor(d[,"c4e3"])
  # Make "*" to NA
c4e3[which(c4e3=="*")]<-"NA"
  levels(c4e3) <- list(Often="1",
                     Sometimes="2",
                     Rarely_Never="3",
                     Dont_know="88")
  c4e3 <- ordered(c4e3, c("Often", "Sometimes", "Rarely_Never", "Dont_know"))
  
  new.d <- data.frame(new.d, c4e3)
  new.d <- apply_labels(new.d, c4e3 = "ask for help-Childhood or young")
  temp.d <- data.frame (new.d, c4e3)
  
  result<-questionr::freq(temp.d$c4e1, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "1. Current (from prostate cancer diagnosis to present)")
1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Often 926 15.9 17.0 15.9 17.0
Sometimes 1854 31.8 34.0 47.6 50.9
Rarely_Never 2189 37.5 40.1 85.1 91.1
Dont_know 488 8.4 8.9 93.5 100.0
NA 382 6.5 NA 100.0 NA
Total 5839 100.0 100.0 100.0 100.0 
  result<-questionr::freq(temp.d$c4e2, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val% %cum val%cum
Often 993 17.0 18.9 17.0 18.9
Sometimes 1979 33.9 37.6 50.9 56.5
Rarely_Never 1860 31.9 35.4 82.8 91.9
Dont_know 428 7.3 8.1 90.1 100.0
NA 579 9.9 NA 100.0 NA
Total 5839 100.0 100.0 100.0 100.0 
  result<-questionr::freq(temp.d$c4e3, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Often 2074 35.5 39.5 35.5 39.5
Sometimes 1585 27.1 30.2 62.7 69.7
Rarely_Never 1099 18.8 20.9 81.5 90.7
Dont_know 489 8.4 9.3 89.9 100.0
NA 592 10.1 NA 100.0 NA
Total 5839 100.0 100.0 100.0 100.0

D1: Treat you because of your race/ethnicity

  • D1. In the following questions, we are interested in your perceptions about the way other people have treated you because of your race/ethnicity or skin color.
      1. At any time in your life, have you ever been unfairly fired from a job or been unfairly denied a promotion?
      1. For unfair reasons, have you ever not been hired for a job?
      1. Have you ever been unfairly stopped, searched, questioned, physically threatened or abused by the police?
      1. Have you ever been unfairly discouraged by a teacher or advisor from continuing your education?
      1. Have you ever been unfairly prevented from moving into a neighborhood because the landlord or a realtor refused to sell or rent you a house or apartment?
      1. Have you ever been unfairly denied a bank loan?
      1. Have you ever been unfairly treated when getting medical care?
      • 1=No
      • 2=Yes
    • If yes, How stressful was this experience?
      • 1=Not at all
      • 2=A little
      • 3=Somewhat
      • 4=Extremely
# a. At any time in your life, have you ever been unfairly fired from a job or been unfairly denied a promotion?
  d1aa <- as.factor(d[,"d1aa"])
# Make "*" to NA
d1aa[which(d1aa=="*")]<-"NA"
  levels(d1aa) <- list(No="1",
                     Yes="2")
  d1aa <- ordered(d1aa, c("No","Yes"))
  
  new.d <- data.frame(new.d, d1aa)
  new.d <- apply_labels(new.d, d1aa = "fired or denied a promotion")
  temp.d <- data.frame (new.d, d1aa)  
  
  d1ab <- as.factor(d[,"d1ab"])
# Make "*" to NA
d1ab[which(d1ab=="*")]<-"NA" 
  levels(d1ab) <- list(Not_at_all="1",
                     A_little="2",
                     Somewhat="3",
                     Extremely="4")
  d1ab <- ordered(d1ab, c("No","Yes"))
  
  new.d <- data.frame(new.d, d1ab)
  new.d <- apply_labels(new.d, d1ab = "fired or denied a promotion-stressful")
  temp.d <- data.frame (new.d, d1ab)
  
  result<-questionr::freq(temp.d$d1aa,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "a. At any time in your life, have you ever been unfairly fired from a job or been unfairly denied a promotion?
")
a. At any time in your life, have you ever been unfairly fired from a job or been unfairly denied a promotion?
n % val%
No 2898 49.6 51.4
Yes 2737 46.9 48.6
NA 204 3.5 NA
Total 5839 100.0 100.0 
  result<-questionr::freq(temp.d$d1ab,total = TRUE,cum=TRUE)
  kable(result, format = "simple", align = 'l', caption = "a. If yes, How stressful was this experience?")
a. If yes, How stressful was this experience?
n % val% %cum val%cum
No 0 0 NaN 0 NaN
Yes 0 0 NaN 0 NaN
NA 5839 100 NA 100 NA
Total 5839 100 100 100 100 
# b. For unfair reasons, have you ever not been hired for a job?
  d1ba <- as.factor(d[,"d1ba"])
  # Make "*" to NA
d1ba[which(d1ba=="*")]<-"NA"
  levels(d1ba) <- list(No="1",
                     Yes="2")
  d1ba <- ordered(d1ba, c("No","Yes"))
  
  new.d <- data.frame(new.d, d1ba)
  new.d <- apply_labels(new.d, d1ba = "not be hired")
  temp.d <- data.frame (new.d, d1ba)  
  
  d1bb <- as.factor(d[,"d1bb"])
  # Make "*" to NA
d1bb[which(d1bb=="*")]<-"NA"
  levels(d1bb) <- list(Not_at_all="1",
                     A_little="2",
                     Somewhat="3",
                     Extremely="4")
  d1bb <- ordered(d1bb, c("No","Yes"))
  
  new.d <- data.frame(new.d, d1bb)
  new.d <- apply_labels(new.d, d1bb = "not be hired-stressful")
  temp.d <- data.frame (new.d, d1bb)
  
  result<-questionr::freq(temp.d$d1ba,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "b. For unfair reasons, have you ever not been hired for a job?")
b. For unfair reasons, have you ever not been hired for a job?
n % val%
No 3393 58.1 61.2
Yes 2153 36.9 38.8
NA 293 5.0 NA
Total 5839 100.0 100.0 
  result<-questionr::freq(temp.d$d1bb,total = TRUE,cum=TRUE)
  kable(result, format = "simple", align = 'l', caption = "b. If yes, How stressful was this experience?")
b. If yes, How stressful was this experience?
n % val% %cum val%cum
No 0 0 NaN 0 NaN
Yes 0 0 NaN 0 NaN
NA 5839 100 NA 100 NA
Total 5839 100 100 100 100 
# c. Have you ever been unfairly stopped, searched, questioned, physically threatened or abused by the police?
  d1ca <- as.factor(d[,"d1ca"])
  # Make "*" to NA
d1ca[which(d1ca=="*")]<-"NA"
  levels(d1ca) <- list(No="1",
                     Yes="2")
  d1ca <- ordered(d1ca, c( "No","Yes"))
  
  new.d <- data.frame(new.d, d1ca)
  new.d <- apply_labels(new.d, d1ca = "By police")
  temp.d <- data.frame (new.d, d1ca)  
  
  result<-questionr::freq(temp.d$d1ca,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "c. Have you ever been unfairly stopped, searched, questioned, physically threatened or abused by the police?")
c. Have you ever been unfairly stopped, searched, questioned, physically threatened or abused by the police?
n % val%
No 2690 46.1 47.8
Yes 2943 50.4 52.2
NA 206 3.5 NA
Total 5839 100.0 100.0 
  d1cb <- as.factor(d[,"d1cb"])
  # Make "*" to NA
d1cb[which(d1cb=="*")]<-"NA"
  levels(d1cb) <- list(Not_at_all="1",
                     A_little="2",
                     Somewhat="3",
                     Extremely="4")
  d1cb <- ordered(d1cb, c("No","Yes"))
  
  new.d <- data.frame(new.d, d1cb)
  new.d <- apply_labels(new.d, d1cb = "By police-stressful")
  temp.d <- data.frame (new.d, d1cb)
  result<-questionr::freq(temp.d$d1cb,total = TRUE,cum=TRUE)
  kable(result, format = "simple", align = 'l', caption = "c. If yes, How stressful was this experience?")
c. If yes, How stressful was this experience?
n % val% %cum val%cum
No 0 0 NaN 0 NaN
Yes 0 0 NaN 0 NaN
NA 5839 100 NA 100 NA
Total 5839 100 100 100 100 
# d. Have you ever been unfairly discouraged by a teacher or advisor from continuing your education?
  d1da <- as.factor(d[,"d1da"])
  # Make "*" to NA
d1da[which(d1da=="*")]<-"NA"
  levels(d1da) <- list(No="1",
                     Yes="2")
  d1da <- ordered(d1da, c( "No","Yes"))
  
  new.d <- data.frame(new.d, d1da)
  new.d <- apply_labels(new.d, d1da = "unfair education")
  temp.d <- data.frame (new.d, d1da)  
  
  result<-questionr::freq(temp.d$d1da,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "d. Have you ever been unfairly discouraged by a teacher or advisor from continuing your education?")
d. Have you ever been unfairly discouraged by a teacher or advisor from continuing your education?
n % val%
No 4413 75.6 78.5
Yes 1211 20.7 21.5
NA 215 3.7 NA
Total 5839 100.0 100.0 
  d1db <- as.factor(d[,"d1db"])
  # Make "*" to NA
d1db[which(d1db=="*")]<-"NA"
  levels(d1db) <- list(Not_at_all="1",
                     A_little="2",
                     Somewhat="3",
                     Extremely="4")
  d1db <- ordered(d1db, c("No","Yes"))
  
  new.d <- data.frame(new.d, d1db)
  new.d <- apply_labels(new.d, d1db = "unfair education-stressful")
  temp.d <- data.frame (new.d, d1db)
  result<-questionr::freq(temp.d$d1db,total = TRUE,cum=TRUE)
  kable(result, format = "simple", align = 'l', caption = "d. If yes, How stressful was this experience?")
d. If yes, How stressful was this experience?
n % val% %cum val%cum
No 0 0 NaN 0 NaN
Yes 0 0 NaN 0 NaN
NA 5839 100 NA 100 NA
Total 5839 100 100 100 100 
# e. Have you ever been unfairly prevented from moving into a neighborhood because the landlord or a realtor refused to sell or rent you a house or apartment?
  d1ea <- as.factor(d[,"d1ea"])
  # Make "*" to NA
d1ea[which(d1ea=="*")]<-"NA"
  levels(d1ea) <- list(No="1",
                     Yes="2")
  d1ea <- ordered(d1ea, c("No","Yes"))
  
  new.d <- data.frame(new.d, d1ea)
  new.d <- apply_labels(new.d, d1ea = "refuse to sell or rent")
  temp.d <- data.frame (new.d, d1ea)  
  
  result<-questionr::freq(temp.d$d1ea,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e. Have you ever been unfairly prevented from moving into a neighborhood because the landlord or a realtor refused to sell or rent you a house or apartment?")
e. Have you ever been unfairly prevented from moving into a neighborhood because the landlord or a realtor refused to sell or rent you a house or apartment?
n % val%
No 4655 79.7 82.3
Yes 1002 17.2 17.7
NA 182 3.1 NA
Total 5839 100.0 100.0 
  d1eb <- as.factor(d[,"d1eb"])
  # Make "*" to NA
d1eb[which(d1eb=="*")]<-"NA"
  levels(d1eb) <- list(Not_at_all="1",
                     A_little="2",
                     Somewhat="3",
                     Extremely="4")
  d1eb <- ordered(d1eb, c("No","Yes"))
  
  new.d <- data.frame(new.d, d1eb)
  new.d <- apply_labels(new.d, d1eb = "refuse to sell or rent-stressful")
  temp.d <- data.frame (new.d, d1eb)
  result<-questionr::freq(temp.d$d1eb,total = TRUE,cum=TRUE)
  kable(result, format = "simple", align = 'l', caption = "e. If yes, How stressful was this experience?")
e. If yes, How stressful was this experience?
n % val% %cum val%cum
No 0 0 NaN 0 NaN
Yes 0 0 NaN 0 NaN
NA 5839 100 NA 100 NA
Total 5839 100 100 100 100 
# f.   Have   you   ever   been   unfairly denied a bank loan?
  d1fa <- as.factor(d[,"d1fa"])
  # Make "*" to NA
d1fa[which(d1fa=="*")]<-"NA"
  levels(d1fa) <- list(No="1",
                     Yes="2")
  d1fa <- ordered(d1fa, c("No","Yes"))
  
  new.d <- data.frame(new.d, d1fa)
  new.d <- apply_labels(new.d, d1fa = "Bank loan")
  temp.d <- data.frame (new.d, d1fa)  
  
  result<-questionr::freq(temp.d$d1fa,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f. Have you ever been unfairly denied a bank loan?")
f. Have you ever been unfairly denied a bank loan?
n % val%
No 4076 69.8 72.7
Yes 1531 26.2 27.3
NA 232 4.0 NA
Total 5839 100.0 100.0 
  d1fb <- as.factor(d[,"d1fb"])
  # Make "*" to NA
d1fb[which(d1fb=="*")]<-"NA"
  levels(d1fb) <- list(Not_at_all="1",
                     A_little="2",
                     Somewhat="3",
                     Extremely="4")
  d1fb <- ordered(d1fb, c("No","Yes"))
  
  new.d <- data.frame(new.d, d1fb)
  new.d <- apply_labels(new.d, d1fb = "Bank loan-stressful")
  temp.d <- data.frame (new.d, d1fb)
  result<-questionr::freq(temp.d$d1fb,total = TRUE,cum=TRUE)
  kable(result, format = "simple", align = 'l', caption = "f. If yes, How stressful was this experience?")
f. If yes, How stressful was this experience?
n % val% %cum val%cum
No 0 0 NaN 0 NaN
Yes 0 0 NaN 0 NaN
NA 5839 100 NA 100 NA
Total 5839 100 100 100 100 
# g.   Have   you   ever   been   unfairly treated when getting medical care?
  d1ga <- as.factor(d[,"d1ga"])
  # Make "*" to NA
d1ga[which(d1ga=="*")]<-"NA"
  levels(d1ga) <- list(No="1",
                     Yes="2")
  d1ga <- ordered(d1ga, c("No","Yes"))
  
  new.d <- data.frame(new.d, d1ga)
  new.d <- apply_labels(new.d, d1ga = "unfair medical care")
  temp.d <- data.frame (new.d, d1ga)  
  
  result<-questionr::freq(temp.d$d1ga,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "g. Have you ever been unfairly treated when getting medical care?")
g. Have you ever been unfairly treated when getting medical care?
n % val%
No 4710 80.7 83.8
Yes 910 15.6 16.2
NA 219 3.8 NA
Total 5839 100.0 100.0 
  d1gb <- as.factor(d[,"d1gb"])
  # Make "*" to NA
d1gb[which(d1gb=="*")]<-"NA"
  levels(d1gb) <- list(Not_at_all="1",
                     A_little="2",
                     Somewhat="3",
                     Extremely="4")
  d1gb <- ordered(d1gb, c("No","Yes"))
  
  new.d <- data.frame(new.d, d1gb)
  new.d <- apply_labels(new.d, d1gb = "unfair medical care-stressful")
  temp.d <- data.frame (new.d, d1gb)
  result<-questionr::freq(temp.d$d1gb,total = TRUE,cum=TRUE)
  kable(result, format = "simple", align = 'l', caption = "g. If yes, How stressful was this experience?")
g. If yes, How stressful was this experience?
n % val% %cum val%cum
No 0 0 NaN 0 NaN
Yes 0 0 NaN 0 NaN
NA 5839 100 NA 100 NA
Total 5839 100 100 100 100

D2: Medical Mistrust

  • D2. These next questions are about your current feelings or perceptions regarding healthcare organizations (places where you might get healthcare, like a hospital or clinic). Indicate your level of agreement or disagreement with each statement.
# a. Patients have sometimes been deceived or misled at hospitals.
  d2a <- as.factor(d[,"d2a"])
# Make "*" to NA
d2a[which(d2a=="*")]<-"NA"
  levels(d2a) <- list(Strongly_Agree="1",
                     Somewhat_Agree="2",
                     Somewhat_Disagree="3",
                     Strongly_Disagree="4")
  d2a <- ordered(d2a, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, d2a)
  new.d <- apply_labels(new.d, d2a = "deceived or misled")
  temp.d <- data.frame (new.d, d2a)  
  
  result<-questionr::freq(temp.d$d2a,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "a. Patients have sometimes been deceived or misled at hospitals.")
a. Patients have sometimes been deceived or misled at hospitals.
n % val%
Strongly_Agree 802 13.7 14.4
Somewhat_Agree 2434 41.7 43.6
Somewhat_Disagree 1332 22.8 23.9
Strongly_Disagree 1009 17.3 18.1
NA 262 4.5 NA
Total 5839 100.0 100.0 
# b. Hospitals often want to know more about your personal affairs or business than they really need to know.
  d2b <- as.factor(d[,"d2b"])
# Make "*" to NA
d2b[which(d2b=="*")]<-"NA"
  levels(d2b) <- list(Strongly_Agree="1",
                     Somewhat_Agree="2",
                     Somewhat_Disagree="3",
                     Strongly_Disagree="4")
  d2b <- ordered(d2b, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, d2b)
  new.d <- apply_labels(new.d, d2b = "personal affairs")
  temp.d <- data.frame (new.d, d2b)  
  
  result<-questionr::freq(temp.d$d2b,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "b. Hospitals often want to know more about your personal affairs or business than they really need to know.")
b. Hospitals often want to know more about your personal affairs or business than they really need to know.
n % val%
Strongly_Agree 711 12.2 12.7
Somewhat_Agree 1952 33.4 34.9
Somewhat_Disagree 1721 29.5 30.8
Strongly_Disagree 1206 20.7 21.6
NA 249 4.3 NA
Total 5839 100.0 100.0 
# c. Hospitals have sometimes done harmful experiments on patients without their knowledge.
  d2c <- as.factor(d[,"d2c"])
# Make "*" to NA
d2c[which(d2c=="*")]<-"NA"
  levels(d2c) <- list(Strongly_Agree="1",
                     Somewhat_Agree="2",
                     Somewhat_Disagree="3",
                     Strongly_Disagree="4")
  d2c <- ordered(d2c, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, d2c)
  new.d <- apply_labels(new.d, d2c = "harmful experiments")
  temp.d <- data.frame (new.d, d2c)  
  
  result<-questionr::freq(temp.d$d2c,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "c. Hospitals have sometimes done harmful experiments on patients without their knowledge.")
c. Hospitals have sometimes done harmful experiments on patients without their knowledge.
n % val%
Strongly_Agree 1067 18.3 19.7
Somewhat_Agree 1812 31.0 33.4
Somewhat_Disagree 1389 23.8 25.6
Strongly_Disagree 1159 19.8 21.4
NA 412 7.1 NA
Total 5839 100.0 100.0 
# d. Rich patients receive better care at hospitals than poor patients.
  d2d <- as.factor(d[,"d2d"])
# Make "*" to NA
d2d[which(d2d=="*")]<-"NA"
  levels(d2d) <- list(Strongly_Agree="1",
                     Somewhat_Agree="2",
                     Somewhat_Disagree="3",
                     Strongly_Disagree="4")
  d2d <- ordered(d2d, c( "Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, d2d)
  new.d <- apply_labels(new.d, d2d = "Rich patients better care")
  temp.d <- data.frame (new.d, d2d)  
  
  result<-questionr::freq(temp.d$d2d,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "d. Rich patients receive better care at hospitals than poor patients.")
d. Rich patients receive better care at hospitals than poor patients.
n % val%
Strongly_Agree 3053 52.3 54.9
Somewhat_Agree 1490 25.5 26.8
Somewhat_Disagree 559 9.6 10.0
Strongly_Disagree 463 7.9 8.3
NA 274 4.7 NA
Total 5839 100.0 100.0 
# e. Male patients receive better care at hospitals than female patients.
  d2e <- as.factor(d[,"d2e"])
# Make "*" to NA
d2e[which(d2e=="*")]<-"NA"
  levels(d2e) <- list(Strongly_Agree="1",
                     Somewhat_Agree="2",
                     Somewhat_Disagree="3",
                     Strongly_Disagree="4")
  d2e <- ordered(d2e, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, d2e)
  new.d <- apply_labels(new.d, d2e = "Male patients better care")
  temp.d <- data.frame (new.d, d2e)  
  
  result<-questionr::freq(temp.d$d2e,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e. Male patients receive better care at hospitals than female patients.")
e. Male patients receive better care at hospitals than female patients.
n % val%
Strongly_Agree 256 4.4 4.7
Somewhat_Agree 869 14.9 16.0
Somewhat_Disagree 2356 40.3 43.4
Strongly_Disagree 1946 33.3 35.9
NA 412 7.1 NA
Total 5839 100.0 100.0

D3A: Treated with less respect

  • D3. In your day-to-day life, during the following 3 time periods, how often have any of the following things happened to you because of your race/ethnicity?
    1. You have been treated with less respect than other people
      1. Current (from prostate cancer diagnosis to the present)
      1. Age 31 up to just before prostate cancer diagnosis
      1. Childhood or young adult life (up to age 30)
      • 1=Never
      • 2=Rarely
      • 3=Sometimes
      • 4=Often
# 1
  d3a1 <- as.factor(d[,"d3a1"])
# Make "*" to NA
d3a1[which(d3a1=="*")]<-"NA"
  levels(d3a1) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3a1 <- ordered(d3a1, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3a1)
  new.d <- apply_labels(new.d, d3a1 = "less respect-current")
  temp.d <- data.frame (new.d, d3a1)  
  
  result<-questionr::freq(temp.d$d3a1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 1646 28.2 28.8
Rarely 1757 30.1 30.7
Sometimes 1975 33.8 34.5
Often 339 5.8 5.9
NA 122 2.1 NA
Total 5839 100.0 100.0 
#2
  d3a2 <- as.factor(d[,"d3a2"])
# Make "*" to NA
d3a2[which(d3a2=="*")]<-"NA"
  levels(d3a2) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3a2 <- ordered(d3a2, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3a2)
  new.d <- apply_labels(new.d, d3a2 = "less respect-31 up")
  temp.d <- data.frame (new.d, d3a2)  
  
  result<-questionr::freq(temp.d$d3a2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 1266 21.7 23.3
Rarely 1605 27.5 29.5
Sometimes 2130 36.5 39.1
Often 442 7.6 8.1
NA 396 6.8 NA
Total 5839 100.0 100.0 
#3
  d3a3 <- as.factor(d[,"d3a3"])
  # Make "*" to NA
d3a3[which(d3a3=="*")]<-"NA"
  levels(d3a3) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3a3 <- ordered(d3a3, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3a3)
  new.d <- apply_labels(new.d, d3a3 = "less respect-child or young")
  temp.d <- data.frame (new.d, d3a3)  
  
  result<-questionr::freq(temp.d$d3a3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 1172 20.1 21.7
Rarely 1205 20.6 22.3
Sometimes 2070 35.5 38.4
Often 947 16.2 17.6
NA 445 7.6 NA
Total 5839 100.0 100.0

D3B: Received poorer service

  • D3. In your day-to-day life, during the following 3 time periods, how often have any of the following things happened to you because of your race/ethnicity?
    1. You have received poorer service than other people at restaurants or stores
      1. Current (from prostate cancer diagnosis to the present)
      1. Age 31 up to just before prostate cancer diagnosis
      1. Childhood or young adult life (up to age 30)
      • 1=Never
      • 2=Rarely
      • 3=Sometimes
      • 4=Often
# 1
  d3b1 <- as.factor(d[,"d3b1"])
# Make "*" to NA
d3b1[which(d3b1=="*")]<-"NA"
  levels(d3b1) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3b1 <- ordered(d3b1, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3b1)
  new.d <- apply_labels(new.d, d3b1 = "poorer service-current")
  temp.d <- data.frame (new.d, d3b1)  
  
  result<-questionr::freq(temp.d$d3b1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 1241 21.3 21.9
Rarely 1954 33.5 34.4
Sometimes 2178 37.3 38.4
Often 304 5.2 5.4
NA 162 2.8 NA
Total 5839 100.0 100.0 
#2
  d3b2 <- as.factor(d[,"d3b2"])
  # Make "*" to NA
d3b2[which(d3b2=="*")]<-"NA"
  levels(d3b2) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3b2 <- ordered(d3b2, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3b2)
  new.d <- apply_labels(new.d, d3b2 = "poorer service-31 up")
  temp.d <- data.frame (new.d, d3b2)  
  
  result<-questionr::freq(temp.d$d3b2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 970 16.6 17.9
Rarely 1686 28.9 31.1
Sometimes 2381 40.8 44.0
Often 379 6.5 7.0
NA 423 7.2 NA
Total 5839 100.0 100.0 
#3
  d3b3 <- as.factor(d[,"d3b3"])
  # Make "*" to NA
d3b3[which(d3b3=="*")]<-"NA"
  levels(d3b3) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3b3 <- ordered(d3b3, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3b3)
  new.d <- apply_labels(new.d, d3b3 = "poorer service-child or young")
  temp.d <- data.frame (new.d, d3b3)  
  
  result<-questionr::freq(temp.d$d3b3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 964 16.5 18.0
Rarely 1270 21.8 23.7
Sometimes 2240 38.4 41.8
Often 884 15.1 16.5
NA 481 8.2 NA
Total 5839 100.0 100.0

D3C: Think you are not smart

  • D3. In your day-to-day life, during the following 3 time periods, how often have any of the following things happened to you because of your race/ethnicity?
    1. People have acted as if they think you are not smart
      1. Current (from prostate cancer diagnosis to the present)
      1. Age 31 up to just before prostate cancer diagnosis
      1. Childhood or young adult life (up to age 30)
      • 1=Never
      • 2=Rarely
      • 3=Sometimes
      • 4=Often
# 1
  d3c1 <- as.factor(d[,"d3c1"])
# Make "*" to NA
d3c1[which(d3c1=="*")]<-"NA"
  levels(d3c1) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3c1 <- ordered(d3c1, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3c1)
  new.d <- apply_labels(new.d, d3c1 = "think you are not smart-current")
  temp.d <- data.frame (new.d, d3c1)  
  
  result<-questionr::freq(temp.d$d3c1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 1567 26.8 27.8
Rarely 1778 30.5 31.5
Sometimes 1853 31.7 32.8
Often 444 7.6 7.9
NA 197 3.4 NA
Total 5839 100.0 100.0 
#2
  d3c2 <- as.factor(d[,"d3c2"])
# Make "*" to NA
d3c2[which(d3c2=="*")]<-"NA"
  levels(d3c2) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3c2 <- ordered(d3c2, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3c2)
  new.d <- apply_labels(new.d, d3c2 = "think you are not smart-31 up")
  temp.d <- data.frame (new.d, d3c2)  
  
  result<-questionr::freq(temp.d$d3c2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 1300 22.3 24.1
Rarely 1693 29.0 31.4
Sometimes 1936 33.2 35.9
Often 461 7.9 8.6
NA 449 7.7 NA
Total 5839 100.0 100.0 
#3
  d3c3 <- as.factor(d[,"d3c3"])
  # Make "*" to NA
d3c3[which(d3c3=="*")]<-"NA"
  levels(d3c3) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3c3 <- ordered(d3c3, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3c3)
  new.d <- apply_labels(new.d, d3c3 = "think you are not smart-child or young")
  temp.d <- data.frame (new.d, d3c3)  
  
  result<-questionr::freq(temp.d$d3c3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 1194 20.4 22.3
Rarely 1374 23.5 25.7
Sometimes 1997 34.2 37.3
Often 783 13.4 14.6
NA 491 8.4 NA
Total 5839 100.0 100.0

D3D: Be afraid of you

  • D3. In your day-to-day life, during the following 3 time periods, how often have any of the following things happened to you because of your race/ethnicity?
    1. People have acted as if they are afraid of you
      1. Current (from prostate cancer diagnosis to the present)
      1. Age 31 up to just before prostate cancer diagnosis
      1. Childhood or young adult life (up to age 30)
      • 1=Never
      • 2=Rarely
      • 3=Sometimes
      • 4=Often
# 1
  d3d1 <- as.factor(d[,"d3d1"])
# Make "*" to NA
d3d1[which(d3d1=="*")]<-"NA"
  levels(d3d1) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3d1 <- ordered(d3d1, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3d1)
  new.d <- apply_labels(new.d, d3d1 = "be afraid of you-current")
  temp.d <- data.frame (new.d, d3d1)  
  
  result<-questionr::freq(temp.d$d3d1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 2051 35.1 36.2
Rarely 1572 26.9 27.7
Sometimes 1681 28.8 29.6
Often 368 6.3 6.5
NA 167 2.9 NA
Total 5839 100.0 100.0 
#2
  d3d2 <- as.factor(d[,"d3d2"])
  # Make "*" to NA
d3d2[which(d3d2=="*")]<-"NA"
  levels(d3d2) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3d2 <- ordered(d3d2, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3d2)
  new.d <- apply_labels(new.d, d3d2 = "be afraid of you-31 up")
  temp.d <- data.frame (new.d, d3d2)  
  
  result<-questionr::freq(temp.d$d3d2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 1647 28.2 30.5
Rarely 1460 25.0 27.0
Sometimes 1812 31.0 33.5
Often 489 8.4 9.0
NA 431 7.4 NA
Total 5839 100.0 100.0 
#3
  d3d3 <- as.factor(d[,"d3d3"])
  # Make "*" to NA
d3d3[which(d3d3=="*")]<-"NA"
  levels(d3d3) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3d3 <- ordered(d3d3, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3d3)
  new.d <- apply_labels(new.d, d3d3 = "be afraid of you-child or young")
  temp.d <- data.frame (new.d, d3d3)  
  
  result<-questionr::freq(temp.d$d3d3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 1752 30.0 32.6
Rarely 1347 23.1 25.1
Sometimes 1627 27.9 30.3
Often 643 11.0 12.0
NA 470 8.0 NA
Total 5839 100.0 100.0

D3E: Think you are dishonest

  • D3. In your day-to-day life, during the following 3 time periods, how often have any of the following things happened to you because of your race/ethnicity?
    1. People have acted as if they think you are dishonest
      1. Current (from prostate cancer diagnosis to the present)
      1. Age 31 up to just before prostate cancer diagnosis
      1. Childhood or young adult life (up to age 30)
      • 1=Never
      • 2=Rarely
      • 3=Sometimes
      • 4=Often
# 1
  d3e1 <- as.factor(d[,"d3e1"])
# Make "*" to NA
d3e1[which(d3e1=="*")]<-"NA"
  levels(d3e1) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3e1 <- ordered(d3e1, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3e1)
  new.d <- apply_labels(new.d, d3e1 = "think you are dishonest-current")
  temp.d <- data.frame (new.d, d3e1)  
  
  result<-questionr::freq(temp.d$d3e1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 2423 41.5 42.8
Rarely 1661 28.4 29.3
Sometimes 1289 22.1 22.8
Often 292 5.0 5.2
NA 174 3.0 NA
Total 5839 100.0 100.0 
#2
  d3e2 <- as.factor(d[,"d3e2"])
  # Make "*" to NA
d3e2[which(d3e2=="*")]<-"NA"
  levels(d3e2) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3e2 <- ordered(d3e2, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3e2)
  new.d <- apply_labels(new.d, d3e2 = "think you are dishonest-31 up")
  temp.d <- data.frame (new.d, d3e2)  
  
  result<-questionr::freq(temp.d$d3e2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 1973 33.8 36.6
Rarely 1602 27.4 29.7
Sometimes 1467 25.1 27.2
Often 355 6.1 6.6
NA 442 7.6 NA
Total 5839 100.0 100.0 
#3
  d3e3 <- as.factor(d[,"d3e3"])
  # Make "*" to NA
d3e3[which(d3e3=="*")]<-"NA"
  levels(d3e3) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3e3 <- ordered(d3e3, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3e3)
  new.d <- apply_labels(new.d, d3e3 = "think you are dishonest-child or young")
  temp.d <- data.frame (new.d, d3e3)  
  
  result<-questionr::freq(temp.d$d3e3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 1894 32.4 35.3
Rarely 1376 23.6 25.6
Sometimes 1516 26.0 28.3
Often 579 9.9 10.8
NA 474 8.1 NA
Total 5839 100.0 100.0

D3F: Better than you

  • D3. In your day-to-day life, during the following 3 time periods, how often have any of the following things happened to you because of your race/ethnicity?
    1. People have acted as if they’re better than you are
      1. Current (from prostate cancer diagnosis to the present)
      1. Age 31 up to just before prostate cancer diagnosis
      1. Childhood or young adult life (up to age 30)
      • 1=Never
      • 2=Rarely
      • 3=Sometimes
      • 4=Often
# 1
  d3f1 <- as.factor(d[,"d3f1"])
# Make "*" to NA
d3f1[which(d3f1=="*")]<-"NA"
  levels(d3f1) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3f1 <- ordered(d3f1, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3f1)
  new.d <- apply_labels(new.d, d3f1 = "better than you-current")
  temp.d <- data.frame (new.d, d3f1)  
  
  result<-questionr::freq(temp.d$d3f1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 898 15.4 15.8
Rarely 1564 26.8 27.6
Sometimes 2480 42.5 43.7
Often 729 12.5 12.9
NA 168 2.9 NA
Total 5839 100.0 100.0 
#2
  d3f2 <- as.factor(d[,"d3f2"])
  # Make "*" to NA
d3f2[which(d3f2=="*")]<-"NA"
  levels(d3f2) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3f2 <- ordered(d3f2, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3f2)
  new.d <- apply_labels(new.d, d3f2 = "better than you-31 up")
  temp.d <- data.frame (new.d, d3f2)  
  
  result<-questionr::freq(temp.d$d3f2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 678 11.6 12.5
Rarely 1371 23.5 25.3
Sometimes 2593 44.4 47.9
Often 776 13.3 14.3
NA 421 7.2 NA
Total 5839 100.0 100.0 
#3
  d3f3 <- as.factor(d[,"d3f3"])
# Make "*" to NA
d3f3[which(d3f3=="*")]<-"NA"
  levels(d3f3) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3f3 <- ordered(d3f3, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3f3)
  new.d <- apply_labels(new.d, d3f3 = "better than you-child or young")
  temp.d <- data.frame (new.d, d3f3)  
  
  result<-questionr::freq(temp.d$d3f3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 679 11.6 12.6
Rarely 1101 18.9 20.5
Sometimes 2355 40.3 43.8
Often 1247 21.4 23.2
NA 457 7.8 NA
Total 5839 100.0 100.0

D3G: Insulted

  • D3. In your day-to-day life, during the following 3 time periods, how often have any of the following things happened to you because of your race/ethnicity?
    1. You have been called names or insulted
      1. Current (from prostate cancer diagnosis to the present)
      1. Age 31 up to just before prostate cancer diagnosis
      1. Childhood or young adult life (up to age 30)
      • 1=Never
      • 2=Rarely
      • 3=Sometimes
      • 4=Often
# 1
  d3g1 <- as.factor(d[,"d3g1"])
# Make "*" to NA
d3g1[which(d3g1=="*")]<-"NA"
  levels(d3g1) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3g1 <- ordered(d3g1, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3g1)
  new.d <- apply_labels(new.d, d3g1 = "called names or insulted-current")
  temp.d <- data.frame (new.d, d3g1)  
  
  result<-questionr::freq(temp.d$d3g1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 2066 35.4 36.5
Rarely 1975 33.8 34.9
Sometimes 1377 23.6 24.4
Often 235 4.0 4.2
NA 186 3.2 NA
Total 5839 100.0 100.0 
#2
  d3g2 <- as.factor(d[,"d3g2"])
  # Make "*" to NA
d3g2[which(d3g2=="*")]<-"NA"
  levels(d3g2) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3g2 <- ordered(d3g2, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3g2)
  new.d <- apply_labels(new.d, d3g2 = "called names or insulted-31 up")
  temp.d <- data.frame (new.d, d3g2)  
  
  result<-questionr::freq(temp.d$d3g2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 1361 23.3 25.2
Rarely 1995 34.2 36.9
Sometimes 1759 30.1 32.5
Often 291 5.0 5.4
NA 433 7.4 NA
Total 5839 100.0 100.0 
#3
  d3g3 <- as.factor(d[,"d3g3"])
  # Make "*" to NA
d3g3[which(d3g3=="*")]<-"NA"
  levels(d3g3) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3g3 <- ordered(d3g3, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3g3)
  new.d <- apply_labels(new.d, d3g3 = "called names or insulted-child or young")
  temp.d <- data.frame (new.d, d3g3)  
  
  result<-questionr::freq(temp.d$d3g3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 928 15.9 17.3
Rarely 1441 24.7 26.8
Sometimes 2186 37.4 40.7
Often 814 13.9 15.2
NA 470 8.0 NA
Total 5839 100.0 100.0

D3H: Threatened or harassed

  • D3. In your day-to-day life, during the following 3 time periods, how often have any of the following things happened to you because of your race/ethnicity?
    1. You have been threatened or harassed
      1. Current (from prostate cancer diagnosis to the present)
      1. Age 31 up to just before prostate cancer diagnosis
      1. Childhood or young adult life (up to age 30)
      • 1=Never
      • 2=Rarely
      • 3=Sometimes
      • 4=Often
# 1
  d3h1 <- as.factor(d[,"d3h1"])
# Make "*" to NA
d3h1[which(d3h1=="*")]<-"NA"
  levels(d3h1) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3h1 <- ordered(d3h1, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3h1)
  new.d <- apply_labels(new.d, d3h1 = "threatened or harassed-current")
  temp.d <- data.frame (new.d, d3h1)  
  
  result<-questionr::freq(temp.d$d3h1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 3133 53.7 55.5
Rarely 1663 28.5 29.5
Sometimes 755 12.9 13.4
Often 91 1.6 1.6
NA 197 3.4 NA
Total 5839 100.0 100.0 
#2
  d3h2 <- as.factor(d[,"d3h2"])
  # Make "*" to NA
d3h2[which(d3e1=="*")]<-"NA"
  levels(d3h2) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3h2 <- ordered(d3h2, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3h2)
  new.d <- apply_labels(new.d, d3h2 = "threatened or harassed-31 up")
  temp.d <- data.frame (new.d, d3h2)  
  
  result<-questionr::freq(temp.d$d3h2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 2314 39.6 43.0
Rarely 1879 32.2 34.9
Sometimes 1038 17.8 19.3
Often 155 2.7 2.9
NA 453 7.8 NA
Total 5839 100.0 100.0 
#3
  d3h3 <- as.factor(d[,"d3h3"])
  # Make "*" to NA
d3h3[which(d3h3=="*")]<-"NA"
  levels(d3h3) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3h3 <- ordered(d3h3, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3h3)
  new.d <- apply_labels(new.d, d3h3 = "threatened or harassed-child or young")
  temp.d <- data.frame (new.d, d3h3)  
  
  result<-questionr::freq(temp.d$d3h3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 1786 30.6 33.4
Rarely 1582 27.1 29.6
Sometimes 1551 26.6 29.0
Often 433 7.4 8.1
NA 487 8.3 NA
Total 5839 100.0 100.0

D3I: Followed around in stores

  • D3. In your day-to-day life, during the following 3 time periods, how often have any of the following things happened to you because of your race/ethnicity?
    1. You have been followed around in stores
      1. Current (from prostate cancer diagnosis to the present)
      1. Age 31 up to just before prostate cancer diagnosis
      1. Childhood or young adult life (up to age 30)
      • 1=Never
      • 2=Rarely
      • 3=Sometimes
      • 4=Often
# 1
  d3i1 <- as.factor(d[,"d3i1"])
# Make "*" to NA
d3i1[which(d3e1=="*")]<-"NA"
  levels(d3i1) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3i1 <- ordered(d3i1, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3i1)
  new.d <- apply_labels(new.d, d3i1 = "be followed-current")
  temp.d <- data.frame (new.d, d3i1)  
  
  result<-questionr::freq(temp.d$d3i1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 2110 36.1 37.3
Rarely 1560 26.7 27.6
Sometimes 1548 26.5 27.4
Often 440 7.5 7.8
NA 181 3.1 NA
Total 5839 100.0 100.0 
#2
  d3i2 <- as.factor(d[,"d3i2"])
  # Make "*" to NA
d3i1[which(d3i1=="*")]<-"NA"
  levels(d3i2) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3i2 <- ordered(d3i2, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3i2)
  new.d <- apply_labels(new.d, d3i2 = "be followed-31 up")
  temp.d <- data.frame (new.d, d3i2)  
  
  result<-questionr::freq(temp.d$d3i2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 1476 25.3 27.3
Rarely 1450 24.8 26.8
Sometimes 1897 32.5 35.1
Often 589 10.1 10.9
NA 427 7.3 NA
Total 5839 100.0 100.0 
#3
  d3i3 <- as.factor(d[,"d3i3"])
  # Make "*" to NA
d3i1[which(d3i1=="*")]<-"NA"
  levels(d3i3) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3i3 <- ordered(d3i3, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3i3)
  new.d <- apply_labels(new.d, d3i3 = "be followed-child or young")
  temp.d <- data.frame (new.d, d3i3)  
  
  result<-questionr::freq(temp.d$d3i3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 1219 20.9 22.7
Rarely 1061 18.2 19.7
Sometimes 1936 33.2 36.0
Often 1163 19.9 21.6
NA 460 7.9 NA
Total 5839 100.0 100.0

D3J: How stressful

  • D3. In your day-to-day life, during the following 3 time periods, how often have any of the following things happened to you because of your race/ethnicity?
    1. How stressful has any of the above experience (a-i) of unfair treatment usually been for you?
      1. Current (from prostate cancer diagnosis to the present)
      1. Age 31 up to just before prostate cancer diagnosis
      1. Childhood or young adult life (up to age 30)
      • 1=Never
      • 2=Rarely
      • 3=Sometimes
      • 4=Often
# 1
  d3j1 <- as.factor(d[,"d3j1"])
# Make "*" to NA
d3j1[which(d3j1=="*")]<-"NA"
  levels(d3j1) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3j1 <- ordered(d3j1, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3j1)
  new.d <- apply_labels(new.d, d3j1 = "How stressful-current")
  temp.d <- data.frame (new.d, d3j1)  
  
  result<-questionr::freq(temp.d$d3j1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 2411 41.3 42.7
Rarely 1893 32.4 33.6
Sometimes 1007 17.2 17.9
Often 329 5.6 5.8
NA 199 3.4 NA
Total 5839 100.0 100.0 
#2
  d3j2 <- as.factor(d[,"d3j2"])
  # Make "*" to NA
d3j2[which(d3j2=="*")]<-"NA"
  levels(d3j2) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3j2 <- ordered(d3j2, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3j2)
  new.d <- apply_labels(new.d, d3j2 = "How stressful-31 up")
  temp.d <- data.frame (new.d, d3j2)  
  
  result<-questionr::freq(temp.d$d3j2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 1819 31.2 33.8
Rarely 1914 32.8 35.5
Sometimes 1256 21.5 23.3
Often 398 6.8 7.4
NA 452 7.7 NA
Total 5839 100.0 100.0 
#3
  d3j3 <- as.factor(d[,"d3j3"])
  # Make "*" to NA
d3j3[which(d3j3=="*")]<-"NA"
  levels(d3j3) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3j3 <- ordered(d3j3, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3j3)
  new.d <- apply_labels(new.d, d3j3 = "How stressful-child or young")
  temp.d <- data.frame (new.d, d3j3)  
  
  result<-questionr::freq(temp.d$d3j3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 1635 28.0 30.5
Rarely 1692 29.0 31.5
Sometimes 1339 22.9 25.0
Often 697 11.9 13.0
NA 476 8.2 NA
Total 5839 100.0 100.0

D4: How you currently see yourself

  • D4. These statements are about how you currently see yourself. Indicate your level of agreement or disagreement with each statement.
      1. You’ve always felt that you could make of your life pretty much what you wanted to make of it.
      1. Once you make up your mind to do something, you stay with it until the job is completely done.
      1. You like doing things that other people thought could not be done.
      1. When things don’t go the way you want them to, that just makes you work even harder.
      1. Sometimes, you feel that if anything is going to be done right, you have to do it yourself.
      1. It’s not always easy, but you manage to find a way to do the things you really need to get done.
      1. Very seldom have you been disappointed by the results of your hard work.
      1. You feel you are the kind of individual who stands up for what he believes in, regardless of the consequences.
      1. In the past, even when things got really tough, you never lost sight of your goals.
      1. It’s important for you to be able to do things the way you want to do them rather than the way other people want you to do them.
      1. You don’t let your personal feelings get in the way of doing a job.
      1. Hard work has really helped you to get ahead in life.
      • 1=Strongly Agree
      • 2=Somewhat Agree
      • 3=Somewhat Disagree
      • 4=Strongly Disagree
# a. You’ve always felt that you could make of your life pretty much what you wanted to make of it.
  d4a <- as.factor(d[,"d4a"])
# Make "*" to NA
d4a[which(d4a=="*")]<-"NA"
  levels(d4a) <- list(Strongly_Agree ="1",
                     Somewhat_Agree="2",
                     Somewhat_Disagree="3",
                     Strongly_Disagree="4")
  d4a <- ordered(d4a, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, d4a)
  new.d <- apply_labels(new.d, d4a = "make life")
  temp.d <- data.frame (new.d, d4a)  
  
  result<-questionr::freq(temp.d$d4a,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "a. You’ve always felt that you could make of your life pretty much what you wanted to make of it.")
a. You’ve always felt that you could make of your life pretty much what you wanted to make of it.
n % val% %cum val%cum
Strongly_Agree 2887 49.4 50.5 49.4 50.5
Somewhat_Agree 2279 39.0 39.8 88.5 90.3
Somewhat_Disagree 450 7.7 7.9 96.2 98.2
Strongly_Disagree 105 1.8 1.8 98.0 100.0
NA 118 2.0 NA 100.0 NA
Total 5839 100.0 100.0 100.0 100.0 
# b. Once you make up your mind to do something, you stay with it until the job is completely done.
  d4b <- as.factor(d[,"d4b"])
  # Make "*" to NA
d4b[which(d4b=="*")]<-"NA"
  levels(d4b) <- list(Strongly_Agree ="1",
                     Somewhat_Agree="2",
                     Somewhat_Disagree="3",
                     Strongly_Disagree="4")
  d4b <- ordered(d4b, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, d4b)
  new.d <- apply_labels(new.d, d4b = "until job is done")
  temp.d <- data.frame (new.d, d4b)  
  
  result<-questionr::freq(temp.d$d4b,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "b. Once you make up your mind to do something, you stay with it until the job is completely done.")
b. Once you make up your mind to do something, you stay with it until the job is completely done.
n % val% %cum val%cum
Strongly_Agree 3727 63.8 65.0 63.8 65.0
Somewhat_Agree 1772 30.3 30.9 94.2 96.0
Somewhat_Disagree 189 3.2 3.3 97.4 99.2
Strongly_Disagree 43 0.7 0.8 98.2 100.0
NA 108 1.8 NA 100.0 NA
Total 5839 100.0 100.0 100.0 100.0 
# c. You like doing things that other people thought could not be done.
  d4c <- as.factor(d[,"d4c"])
  # Make "*" to NA
d4c[which(d4c=="*")]<-"NA"
  levels(d4c) <- list(Strongly_Agree ="1",
                     Somewhat_Agree="2",
                     Somewhat_Disagree="3",
                     Strongly_Disagree="4")
  d4c <- ordered(d4c, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, d4c)
  new.d <- apply_labels(new.d, d4c = "until job is done")
  temp.d <- data.frame (new.d, d4c)  
  
  result<-questionr::freq(temp.d$d4c,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "c. You like doing things that other people thought could not be done.")
c. You like doing things that other people thought could not be done.
n % val% %cum val%cum
Strongly_Agree 2751 47.1 48.3 47.1 48.3
Somewhat_Agree 2273 38.9 39.9 86.0 88.2
Somewhat_Disagree 560 9.6 9.8 95.6 98.0
Strongly_Disagree 114 2.0 2.0 97.6 100.0
NA 141 2.4 NA 100.0 NA
Total 5839 100.0 100.0 100.0 100.0 
# d. When things don’t go the way you want them to, that just makes you work even harder.
  d4d <- as.factor(d[,"d4d"])
  # Make "*" to NA
d4d[which(d4d=="*")]<-"NA"
  levels(d4d) <- list(Strongly_Agree ="1",
                     Somewhat_Agree="2",
                     Somewhat_Disagree="3",
                     Strongly_Disagree="4")
  d4d <- ordered(d4d, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, d4d)
  new.d <- apply_labels(new.d, d4d = "until job is done")
  temp.d <- data.frame (new.d, d4d)  
  
  result<-questionr::freq(temp.d$d4d,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "d. When things don’t go the way you want them to, that just makes you work even harder.")
d. When things don’t go the way you want them to, that just makes you work even harder.
n % val% %cum val%cum
Strongly_Agree 2755 47.2 48.2 47.2 48.2
Somewhat_Agree 2404 41.2 42.1 88.4 90.3
Somewhat_Disagree 468 8.0 8.2 96.4 98.5
Strongly_Disagree 84 1.4 1.5 97.8 100.0
NA 128 2.2 NA 100.0 NA
Total 5839 100.0 100.0 100.0 100.0 
# e. Sometimes, you feel that if anything is going to be done right, you have to do it yourself.
  d4e <- as.factor(d[,"d4e"])
  # Make "*" to NA
d4e[which(d4e=="*")]<-"NA"
  levels(d4e) <- list(Strongly_Agree ="1",
                     Somewhat_Agree="2",
                     Somewhat_Disagree="3",
                     Strongly_Disagree="4")
  d4e <- ordered(d4e, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, d4e)
  new.d <- apply_labels(new.d, d4e = "do it yourself")
  temp.d <- data.frame (new.d, d4e)  
  
  result<-questionr::freq(temp.d$d4e,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e. Sometimes, you feel that if anything is going to be done right, you have to do it yourself.")
e. Sometimes, you feel that if anything is going to be done right, you have to do it yourself.
n % val% %cum val%cum
Strongly_Agree 2445 41.9 42.7 41.9 42.7
Somewhat_Agree 2281 39.1 39.8 80.9 82.5
Somewhat_Disagree 824 14.1 14.4 95.1 96.9
Strongly_Disagree 176 3.0 3.1 98.1 100.0
NA 113 1.9 NA 100.0 NA
Total 5839 100.0 100.0 100.0 100.0 
# f. It’s not always easy, but you manage to find a way to do the things you really need to get done.
  d4f <- as.factor(d[,"d4f"])
  # Make "*" to NA
d4f[which(d4f=="*")]<-"NA"
  levels(d4f) <- list(Strongly_Agree ="1",
                     Somewhat_Agree="2",
                     Somewhat_Disagree="3",
                     Strongly_Disagree="4")
  d4f <- ordered(d4f, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, d4f)
  new.d <- apply_labels(new.d, d4f = "not easy but get it done")
  temp.d <- data.frame (new.d, d4f)  
  
  result<-questionr::freq(temp.d$d4f,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f. It’s not always easy, but you manage to find a way to do the things you really need to get done.")
f. It’s not always easy, but you manage to find a way to do the things you really need to get done.
n % val% %cum val%cum
Strongly_Agree 3720 63.7 64.9 63.7 64.9
Somewhat_Agree 1852 31.7 32.3 95.4 97.3
Somewhat_Disagree 116 2.0 2.0 97.4 99.3
Strongly_Disagree 41 0.7 0.7 98.1 100.0
NA 110 1.9 NA 100.0 NA
Total 5839 100.0 100.0 100.0 100.0 
# g. Very seldom have you been disappointed by the results of your hard work.
  d4g <- as.factor(d[,"d4g"])
  # Make "*" to NA
d4g[which(d4g=="*")]<-"NA"
  levels(d4g) <- list(Strongly_Agree ="1",
                     Somewhat_Agree="2",
                     Somewhat_Disagree="3",
                     Strongly_Disagree="4")
  d4g <- ordered(d4g, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, d4g)
  new.d <- apply_labels(new.d, d4g = "seldom disappointed")
  temp.d <- data.frame (new.d, d4g)  
  
  result<-questionr::freq(temp.d$d4g,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "g. Very seldom have you been disappointed by the results of your hard work.")
g. Very seldom have you been disappointed by the results of your hard work.
n % val% %cum val%cum
Strongly_Agree 2093 35.8 36.7 35.8 36.7
Somewhat_Agree 2633 45.1 46.2 80.9 82.9
Somewhat_Disagree 742 12.7 13.0 93.6 95.9
Strongly_Disagree 233 4.0 4.1 97.6 100.0
NA 138 2.4 NA 100.0 NA
Total 5839 100.0 100.0 100.0 100.0 
# h. You feel you are the kind of individual who stands up for what he believes in, regardless of the consequences.
  d4h <- as.factor(d[,"d4h"])
  # Make "*" to NA
d4h[which(d4h=="*")]<-"NA"
  levels(d4h) <- list(Strongly_Agree ="1",
                     Somewhat_Agree="2",
                     Somewhat_Disagree="3",
                     Strongly_Disagree="4")
  d4h <- ordered(d4h, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, d4h)
  new.d <- apply_labels(new.d, d4h = "stand up for believes")
  temp.d <- data.frame (new.d, d4h)  
  
  result<-questionr::freq(temp.d$d4h,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "h. You feel you are the kind of individual who stands up for what he believes in, regardless of the consequences.")
h. You feel you are the kind of individual who stands up for what he believes in, regardless of the consequences.
n % val% %cum val%cum
Strongly_Agree 3538 60.6 61.9 60.6 61.9
Somewhat_Agree 1874 32.1 32.8 92.7 94.6
Somewhat_Disagree 257 4.4 4.5 97.1 99.1
Strongly_Disagree 49 0.8 0.9 97.9 100.0
NA 121 2.1 NA 100.0 NA
Total 5839 100.0 100.0 100.0 100.0 
# i. In the past, even when things got really tough, you never lost sight of your goals.
  d4i <- as.factor(d[,"d4i"])
    # Make "*" to NA
d4i[which(d4i=="*")]<-"NA"
  levels(d4i) <- list(Strongly_Agree ="1",
                     Somewhat_Agree="2",
                     Somewhat_Disagree="3",
                     Strongly_Disagree="4")
  d4i <- ordered(d4i, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, d4i)
  new.d <- apply_labels(new.d, d4i = "tough but never lost")
  temp.d <- data.frame (new.d, d4i)  
  
  result<-questionr::freq(temp.d$d4i,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "i. In the past, even when things got really tough, you never lost sight of your goals.")
i. In the past, even when things got really tough, you never lost sight of your goals.
n % val% %cum val%cum
Strongly_Agree 3279 56.2 57.3 56.2 57.3
Somewhat_Agree 2039 34.9 35.7 91.1 93.0
Somewhat_Disagree 343 5.9 6.0 97.0 99.0
Strongly_Disagree 58 1.0 1.0 97.9 100.0
NA 120 2.1 NA 100.0 NA
Total 5839 100.0 100.0 100.0 100.0 
#j. It’s important for you to be able to do things the way you want to do them rather than the way other people want you to do them.
  d4j <- as.factor(d[,"d4j"])
    # Make "*" to NA
d4j[which(d4j=="*")]<-"NA"
  levels(d4j) <- list(Strongly_Agree ="1",
                     Somewhat_Agree="2",
                     Somewhat_Disagree="3",
                     Strongly_Disagree="4")
  d4j <- ordered(d4j, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, d4j)
  new.d <- apply_labels(new.d, d4j = "the way you want to do matters")
  temp.d <- data.frame (new.d, d4j)  
  
  result<-questionr::freq(temp.d$d4j,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "j. It’s important for you to be able to do things the way you want to do them rather than the way other people want you to do them.")
j. It’s important for you to be able to do things the way you want to do them rather than the way other people want you to do them.
n % val% %cum val%cum
Strongly_Agree 1987 34.0 34.7 34.0 34.7
Somewhat_Agree 2314 39.6 40.5 73.7 75.2
Somewhat_Disagree 1180 20.2 20.6 93.9 95.8
Strongly_Disagree 238 4.1 4.2 97.9 100.0
NA 120 2.1 NA 100.0 NA
Total 5839 100.0 100.0 100.0 100.0 
#k. You don’t let your personal feelings get in the way of doing a job.
  d4k <- as.factor(d[,"d4k"])
    # Make "*" to NA
d4k[which(d4k=="*")]<-"NA"
  levels(d4k) <- list(Strongly_Agree ="1",
                     Somewhat_Agree="2",
                     Somewhat_Disagree="3",
                     Strongly_Disagree="4")
  d4k <- ordered(d4k, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, d4k)
  new.d <- apply_labels(new.d, d4k = "personal feelings never get in the way of job")
  temp.d <- data.frame (new.d, d4k)  
  
  result<-questionr::freq(temp.d$d4k,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "k. You don’t let your personal feelings get in the way of doing a job.")
k. You don’t let your personal feelings get in the way of doing a job.
n % val% %cum val%cum
Strongly_Agree 3015 51.6 52.7 51.6 52.7
Somewhat_Agree 2173 37.2 38.0 88.9 90.7
Somewhat_Disagree 429 7.3 7.5 96.2 98.1
Strongly_Disagree 106 1.8 1.9 98.0 100.0
NA 116 2.0 NA 100.0 NA
Total 5839 100.0 100.0 100.0 100.0 
#l. Hard work has really helped you to get ahead in life.
  d4l <- as.factor(d[,"d4l"])
    # Make "*" to NA
d4l[which(d4l=="*")]<-"NA"
  levels(d4l) <- list(Strongly_Agree ="1",
                     Somewhat_Agree="2",
                     Somewhat_Disagree="3",
                     Strongly_Disagree="4")
  d4l <- ordered(d4l, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, d4l)
  new.d <- apply_labels(new.d, d4l = "hard work helps")
  temp.d <- data.frame (new.d, d4l)  
  
  result<-questionr::freq(temp.d$d4l,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "l. Hard work has really helped you to get ahead in life.")
l. Hard work has really helped you to get ahead in life.
n % val% %cum val%cum
Strongly_Agree 3903 66.8 68.1 66.8 68.1
Somewhat_Agree 1466 25.1 25.6 92.0 93.7
Somewhat_Disagree 278 4.8 4.9 96.7 98.6
Strongly_Disagree 83 1.4 1.4 98.1 100.0
NA 109 1.9 NA 100.0 NA
Total 5839 100.0 100.0 100.0 100.0

D5: Childhood

  • D5. The next questions are about the time period of your childhood, before the age of 18. These are standard questions asked in many surveys of life history. This information will allow us to understand how problems that may occur early in life may affect health later in life. This is a sensitive topic and some people may feel uncomfortable with these questions. Please keep in mind that you can skip any question you do not want to answer. All information is kept confidential. When you were growing up, during the first 18 years of your life…
    1. Did you live with anyone who was depressed, mentally ill, or suicidal?
    1. Did you live with anyone who was a problem drinker or alcoholic?
    1. Did you live with anyone who used illegal street drugs or who abused prescription medications?
    1. Did you live with anyone who served time or was sentenced to serve time in a prison, jail, or other correctional facility?
    1. Were your parents separated or divorced?
    1. How often did your parents or adults in your home ever slap, hit, kick, punch or beat each other up?
    1. How often did a parent or adult in your home ever hit, beat, kick, or physically hurt you in any way? Do not include spanking.
    1. How often did a parent or adult in your home ever swear at you, insult you, or put you down?
    1. How often did anyone at least 5 years older than you or an adult, ever touch you sexually?
    1. How often did anyone at least 5 years older than you or an adult, try to make you touch them sexually?
    1. How often did anyone at least 5 years older than you or an adult, force you to have sex?
    • 1=No
    • 2=Yes
    • 3=Parents not married
    • 88=Don’t know/not sure
    • 99=Prefer not to answer”
# a. Did you live with anyone who was depressed, mentally ill, or suicidal?
  d5a <- as.factor(d[,"d5a"])
  # Make "*" to NA
d5a[which(d5a=="*")]<-"NA"
  levels(d5a) <- list(No="1",
                     Yes="2",
                     Dont_know_not_sure="88",
                     Prefer_not_to_answer="99")
  d5a <- ordered(d5a, c("No","Yes","Dont_know_not_sure","Prefer_not_to_answer"))
  
  new.d <- data.frame(new.d, d5a)
  new.d <- apply_labels(new.d, d5a = "live with depressed")
  temp.d <- data.frame (new.d, d5a)  
  
  result<-questionr::freq(temp.d$d5a,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "a. Did you live with anyone who was depressed, mentally ill, or suicidal?")
a. Did you live with anyone who was depressed, mentally ill, or suicidal?
n % val%
No 4739 81.2 82.5
Yes 530 9.1 9.2
Dont_know_not_sure 423 7.2 7.4
Prefer_not_to_answer 51 0.9 0.9
NA 96 1.6 NA
Total 5839 100.0 100.0 
# b. Did you live with anyone who was a problem drinker or alcoholic?
  d5b <- as.factor(d[,"d5b"])
# Make "*" to NA
d5b[which(d5b=="*")]<-"NA"
  levels(d5b) <- list(No="1",
                     Yes="2",
                     Dont_know_not_sure="88",
                     Prefer_not_to_answer="99")
  d5b <- ordered(d5b, c( "No","Yes","Dont_know_not_sure","Prefer_not_to_answer"))
  
  new.d <- data.frame(new.d, d5b)
  new.d <- apply_labels(new.d, d5b = "live with alcoholic")
  temp.d <- data.frame (new.d, d5b)  
  
  result<-questionr::freq(temp.d$d5b,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "b. Did you live with anyone who was a problem drinker or alcoholic?")
b. Did you live with anyone who was a problem drinker or alcoholic?
n % val%
No 3942 67.5 68.6
Yes 1484 25.4 25.8
Dont_know_not_sure 236 4.0 4.1
Prefer_not_to_answer 84 1.4 1.5
NA 93 1.6 NA
Total 5839 100.0 100.0 
# c. Did you live with anyone who used illegal street drugs or who abused prescription medications?  
  d5c <- as.factor(d[,"d5c"])
# Make "*" to NA
d5c[which(d5c=="*")]<-"NA"
  levels(d5c) <- list(No="1",
                     Yes="2",
                     Dont_know_not_sure="88",
                     Prefer_not_to_answer="99")
  d5c <- ordered(d5c, c( "No","Yes","Dont_know_not_sure","Prefer_not_to_answer"))
  
  new.d <- data.frame(new.d, d5c)
  new.d <- apply_labels(new.d, d5c = "live with illegal street drugs")
  temp.d <- data.frame (new.d, d5c)  
  
  result<-questionr::freq(temp.d$d5c,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "c. Did you live with anyone who used illegal street drugs or who abused prescription medications?")
c. Did you live with anyone who used illegal street drugs or who abused prescription medications?
n % val%
No 4863 83.3 84.8
Yes 566 9.7 9.9
Dont_know_not_sure 249 4.3 4.3
Prefer_not_to_answer 60 1.0 1.0
NA 101 1.7 NA
Total 5839 100.0 100.0 
# d. Did you live with anyone who served time or was sentenced to serve time in a prison, jail, or other correctional facility? 
  d5d <- as.factor(d[,"d5d"])
# Make "*" to NA
d5d[which(d5d=="*")]<-"NA"
  levels(d5d) <- list(No="1",
                     Yes="2",
                     Dont_know_not_sure="88",
                     Prefer_not_to_answer="99")
  d5d <- ordered(d5d, c( "No","Yes","Dont_know_not_sure","Prefer_not_to_answer"))
  
  new.d <- data.frame(new.d, d5d)
  new.d <- apply_labels(new.d, d5d = "live with people in a prison")
  temp.d <- data.frame (new.d, d5d)  
  
  result<-questionr::freq(temp.d$d5d,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "d. Did you live with anyone who served time or was sentenced to serve time in a prison, etc?")
d. Did you live with anyone who served time or was sentenced to serve time in a prison, etc?
n % val%
No 4986 85.4 86.9
Yes 614 10.5 10.7
Dont_know_not_sure 81 1.4 1.4
Prefer_not_to_answer 55 0.9 1.0
NA 103 1.8 NA
Total 5839 100.0 100.0 
# e. Were your parents separated or divorced? 
  d5e <- as.factor(d[,"d5e"])
# Make "*" to NA
d5e[which(d5e=="*")]<-"NA"
  levels(d5e) <- list(No="1",
                     Yes="2",
                     Not_married="3",
                     Dont_know_not_sure="88",
                     Prefer_not_to_answer="99")
  d5e <- ordered(d5e, c( "No","Yes","Not_married","Dont_know_not_sure","Prefer_not_to_answer"))
  
  new.d <- data.frame(new.d, d5e)
  new.d <- apply_labels(new.d, d5e = "parents divorced")
  temp.d <- data.frame (new.d, d5e)  
  
  result<-questionr::freq(temp.d$d5e,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e. Were your parents separated or divorced?")
e. Were your parents separated or divorced?
n % val%
No 3442 58.9 60.2
Yes 1639 28.1 28.6
Not_married 468 8.0 8.2
Dont_know_not_sure 68 1.2 1.2
Prefer_not_to_answer 104 1.8 1.8
NA 118 2.0 NA
Total 5839 100.0 100.0 
# f. How often did your parents or adults in your home ever slap, hit, kick, punch or beat each other up?
  d5f <- as.factor(d[,"d5f"])
# Make "*" to NA
d5f[which(d5f=="*")]<-"NA"
  levels(d5f) <- list(Never="1",
                     Once="2",
                     More_than_once="3",
                     Dont_know_not_sure="88",
                     Prefer_not_to_answer="99")
  d5f <- ordered(d5f, c("Never", "Once","More_than_once","Dont_know_not_sure","Prefer_not_to_answer"))
  
  new.d <- data.frame(new.d, d5f)
  new.d <- apply_labels(new.d, d5f = "violence to each other")
  temp.d <- data.frame (new.d, d5f)  
  
  result<-questionr::freq(temp.d$d5f,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f. How often did your parents or adults in your home ever slap, hit, kick, punch or beat each other up?")
f. How often did your parents or adults in your home ever slap, hit, kick, punch or beat each other up?
n % val%
Never 3569 61.1 62.7
Once 375 6.4 6.6
More_than_once 905 15.5 15.9
Dont_know_not_sure 614 10.5 10.8
Prefer_not_to_answer 232 4.0 4.1
NA 144 2.5 NA
Total 5839 100.0 100.0 
#  g. How often did a parent or adult in your home ever hit, beat, kick, or physically hurt you in any way?
  d5g <- as.factor(d[,"d5g"])
# Make "*" to NA
d5g[which(d5g=="*")]<-"NA"
  levels(d5g) <- list(Never="1",
                     Once="2",
                     More_than_once="3",
                     Dont_know_not_sure="88",
                     Prefer_not_to_answer="99")
  d5g <- ordered(d5g, c("Never", "Once","More_than_once","Dont_know_not_sure","Prefer_not_to_answer"))
  
  new.d <- data.frame(new.d, d5g)
  new.d <- apply_labels(new.d, d5g = "violence to you")
  temp.d <- data.frame (new.d, d5g)  
  
  result<-questionr::freq(temp.d$d5g,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "g. How often did a parent or adult in your home ever hit, beat, kick, or physically hurt you in any way?")
g. How often did a parent or adult in your home ever hit, beat, kick, or physically hurt you in any way?
n % val%
Never 4227 72.4 74.1
Once 221 3.8 3.9
More_than_once 868 14.9 15.2
Dont_know_not_sure 208 3.6 3.6
Prefer_not_to_answer 179 3.1 3.1
NA 136 2.3 NA
Total 5839 100.0 100.0 
# h. How often did a parent or adult in your home ever swear at you, insult you, or put you down?
  d5h <- as.factor(d[,"d5h"])
# Make "*" to NA
d5h[which(d5h=="*")]<-"NA"
  levels(d5h) <- list(Never="1",
                     Once="2",
                     More_than_once="3",
                     Dont_know_not_sure="88",
                     Prefer_not_to_answer="99")
  d5h <- ordered(d5h, c("Never", "Once","More_than_once","Dont_know_not_sure","Prefer_not_to_answer"))
  
  new.d <- data.frame(new.d, d5h)
  new.d <- apply_labels(new.d, d5h = "swear insult")
  temp.d <- data.frame (new.d, d5h)  
  
  result<-questionr::freq(temp.d$d5h,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "h. How often did a parent or adult in your home ever swear at you, insult you, or put you down?")
h. How often did a parent or adult in your home ever swear at you, insult you, or put you down?
n % val%
Never 3485 59.7 61.0
Once 263 4.5 4.6
More_than_once 1395 23.9 24.4
Dont_know_not_sure 398 6.8 7.0
Prefer_not_to_answer 168 2.9 2.9
NA 130 2.2 NA
Total 5839 100.0 100.0 
# i. How often did anyone at least 5 years older than you or an adult, ever touch you sexually?
  d5i <- as.factor(d[,"d5i"])
  # Make "*" to NA
d5i[which(d5i=="*")]<-"NA"
  levels(d5i) <- list(Never="1",
                     Once="2",
                     More_than_once="3",
                     Dont_know_not_sure="88",
                     Prefer_not_to_answer="99")
  d5i <- ordered(d5i, c("Never", "Once","More_than_once","Dont_know_not_sure","Prefer_not_to_answer"))
  
  new.d <- data.frame(new.d, d5i)
  new.d <- apply_labels(new.d, d5i = "touch you sexually")
  temp.d <- data.frame (new.d, d5i)  
  
  result<-questionr::freq(temp.d$d5i,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "i. How often did anyone at least 5 years older than you or an adult, ever touch you sexually?")
i. How often did anyone at least 5 years older than you or an adult, ever touch you sexually?
n % val%
Never 5134 87.9 89.6
Once 248 4.2 4.3
More_than_once 197 3.4 3.4
Dont_know_not_sure 70 1.2 1.2
Prefer_not_to_answer 78 1.3 1.4
NA 112 1.9 NA
Total 5839 100.0 100.0 
# j. How often did anyone at least 5 years older than you or an adult, try to make you touch them sexually?
  d5j <- as.factor(d[,"d5j"])
  # Make "*" to NA
d5j[which(d5j=="*")]<-"NA"
  levels(d5j) <- list(Never="1",
                     Once="2",
                     More_than_once="3",
                     Dont_know_not_sure="88",
                     Prefer_not_to_answer="99")
  d5j <- ordered(d5j, c("Never","Once","More_than_once","Dont_know_not_sure","Prefer_not_to_answer"))
  
  new.d <- data.frame(new.d, d5j)
  new.d <- apply_labels(new.d, d5j = "touch them sexually")
  temp.d <- data.frame (new.d, d5j)  
  
  result<-questionr::freq(temp.d$d5j,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "j. How often did anyone at least 5 years older than you or an adult, try to make you touch them sexually?")
j. How often did anyone at least 5 years older than you or an adult, try to make you touch them sexually?
n % val%
Never 5214 89.3 91.0
Once 217 3.7 3.8
More_than_once 167 2.9 2.9
Dont_know_not_sure 57 1.0 1.0
Prefer_not_to_answer 75 1.3 1.3
NA 109 1.9 NA
Total 5839 100.0 100.0 
# k. How often did anyone at least 5 years older than you or an adult, force you to have sex?
  d5k <- as.factor(d[,"d5k"])
  # Make "*" to NA
d5k[which(d5k=="*")]<-"NA"
  levels(d5k) <- list(Never="1",
                     Once="2",
                     More_than_once="3",
                     Dont_know_not_sure="88",
                     Prefer_not_to_answer="99")
  d5k <- ordered(d5k, c("Never","Once","More_than_once","Dont_know_not_sure","Prefer_not_to_answer"))
  
  new.d <- data.frame(new.d, d5k)
  new.d <- apply_labels(new.d, d5k = "forced to have sex")
  temp.d <- data.frame (new.d, d5k)  
  
  result<-questionr::freq(temp.d$d5k,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "k. How often did anyone at least 5 years older than you or an adult, force you to have sex?")
k. How often did anyone at least 5 years older than you or an adult, force you to have sex?
n % val%
Never 5412 92.7 94.4
Once 100 1.7 1.7
More_than_once 106 1.8 1.8
Dont_know_not_sure 43 0.7 0.8
Prefer_not_to_answer 70 1.2 1.2
NA 108 1.8 NA
Total 5839 100.0 100.0

E1: First indications

  • E1. What were the first indications that suggested that you might have prostate cancer (before you had a prostate biopsy)? Mark all that apply.
    • E1_1: 1=I had a high PSA (‘prostate specific antigen’) test
    • E1_2: 1=My doctor did a digital rectal exam that indicated an abnormality
    • E1_3: 1=I had urinary, sexual, or bowel problems that I went to see my doctor about
    • E1_4: 1=I had bone pain that I went to see my doctor about
    • E1_5: 1=I was fearful I had cancer
    • E1_6: 1=Other
# 1
  e1_1 <- as.factor(d[,"e1_1"])
  levels(e1_1) <- list(High_PSA_test="1")

  new.d <- data.frame(new.d, e1_1)
  new.d <- apply_labels(new.d, e1_1 = "High_PSA_test")
  temp.d <- data.frame (new.d, e1_1)  
  
  result<-questionr::freq(temp.d$e1_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. I had a high PSA (‘prostate specific antigen’) test")
1. I had a high PSA (‘prostate specific antigen’) test
n % val%
High_PSA_test 4540 77.8 100
NA 1299 22.2 NA
Total 5839 100.0 100 
#2
  e1_2 <- as.factor(d[,"e1_2"])
  levels(e1_2) <- list(Digital_rectal_exam="1")

  new.d <- data.frame(new.d, e1_2)
  new.d <- apply_labels(new.d, e1_2 = "digital rectal exam")
  temp.d <- data.frame (new.d, e1_2)  
  
  result<-questionr::freq(temp.d$e1_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. My doctor did a digital rectal exam that indicated an abnormality")
2. My doctor did a digital rectal exam that indicated an abnormality
n % val%
Digital_rectal_exam 1584 27.1 100
NA 4255 72.9 NA
Total 5839 100.0 100 
#3
  e1_3 <- as.factor(d[,"e1_3"])
  e1_3[which(e1_3=="*")]<-"NA"
  levels(e1_3) <- list(Digital_rectal_exam="1")

  new.d <- data.frame(new.d, e1_3)
  new.d <- apply_labels(new.d, e1_3 = "urinary sexual or bowel problems")
  temp.d <- data.frame (new.d, e1_3)  
  
  result<-questionr::freq(temp.d$e1_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. I had urinary, sexual, or bowel problems that I went to see my doctor about")
3. I had urinary, sexual, or bowel problems that I went to see my doctor about
n % val%
Digital_rectal_exam 1054 18.1 100
NA 4785 81.9 NA
Total 5839 100.0 100 
#4
  e1_4 <- as.factor(d[,"e1_4"])
  e1_4[which(e1_4=="*")]<-"NA"
  levels(e1_4) <- list(Digital_rectal_exam="1")

  new.d <- data.frame(new.d, e1_4)
  new.d <- apply_labels(new.d, e1_4 = "bone pain")
  temp.d <- data.frame (new.d, e1_4)  
  
  result<-questionr::freq(temp.d$e1_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "4. I had bone pain that I went to see my doctor about")
4. I had bone pain that I went to see my doctor about
n % val%
Digital_rectal_exam 90 1.5 100
NA 5749 98.5 NA
Total 5839 100.0 100 
#5
  e1_5 <- as.factor(d[,"e1_5"])
  e1_5[which(e1_5=="*")]<-"NA"
  levels(e1_5) <- list(Digital_rectal_exam="1")

  new.d <- data.frame(new.d, e1_5)
  new.d <- apply_labels(new.d, e1_5 = "fearful")
  temp.d <- data.frame (new.d, e1_5)  
  
  result<-questionr::freq(temp.d$e1_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "5. I was fearful I had cancer")
5. I was fearful I had cancer
n % val%
Digital_rectal_exam 233 4 100
NA 5606 96 NA
Total 5839 100 100

E1 Other: First indications

e1other <- d[,"e1other"]
e1other[which(e1other=="#NAME?")]<-"NA"

  new.d <- data.frame(new.d, e1other)
  new.d <- apply_labels(new.d, e1other = "e1other")
  temp.d <- data.frame (new.d, e1other)
result<-questionr::freq(temp.d$e1other, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "E1 Other")
E1 Other
n % val%
2 cousins died with PC before my increasing PSA test 1 0.0 0.2
3 maternal uncles have prostate cancer 1 0.0 0.2
51 or 52 1 0.0 0.2
A bladder infection 1 0.0 0.2
A friend knew of my pain and suggested I attent Prostate Survisors group 1 0.0 0.2
A low blood count 1 0.0 0.2
A sopt was observed on my Lung after a CT Scand that turned out to be Metastatic. I was diagnosed after receiving a Postate Biopsy. 1 0.0 0.2
Accident. Took father-in-law to doctor. Asked me if he examine me. I said yes. That’s it. 1 0.0 0.2
Acupuncturist referred me to my doctor. 1 0.0 0.2
After bladder infection 1 0.0 0.2
After having many high PSA blood tests I got a positive biopsy test. 1 0.0 0.2
After I had been hurt moving a heavy object is when I found out I had prostate cancer. 1 0.0 0.2
After my oldest brother died of cancer, my doctor suggested a biopsy 1 0.0 0.2
Age 50 I took a blood test 1 0.0 0.2
age and race 1 0.0 0.2
Agent Orange Vietnam 1972 1 0.0 0.2
Always weak. 1 0.0 0.2
An older brother. Tells me what was the symptoms of prostate cancer and made an appointment to see a doctor. 1 0.0 0.2
And was feeling pain below my belly and frequent urination 1 0.0 0.2
annual exam 1 0.0 0.2
Annual Examination 1 0.0 0.2
Annual physical 3 0.1 0.7
As a result of a physical exam 1 0.0 0.2
As a Vietnam Veteran, I was assigned to an agent orange area. 1 0.0 0.2
Asked for check-up. 1 0.0 0.2
At a routine check-up. 1 0.0 0.2
At my age, was advised to have exam. 1 0.0 0.2
Back pain, getting married blood test. 1 0.0 0.2
Back pain/stomach pain 1 0.0 0.2
Back problems. 1 0.0 0.2
Be check 1 0.0 0.2
Bence Jones protein 1 0.0 0.2
biopsy 1 0.0 0.2
Biopsy 1 0.0 0.2
Biopsy, MRI 1 0.0 0.2
Bladder condition. 1 0.0 0.2
Bladder Neck Surgery, Prostate Resection 1 0.0 0.2
Bled once during 1 0.0 0.2
Bleeding from rectum/blood in urine 1 0.0 0.2
Bleeding in urine after sex. 1 0.0 0.2
Blood drive 1 0.0 0.2
Blood in my ejaculation/sperm 1 0.0 0.2
Blood in my urine 1 0.0 0.2
Blood in my urine January 2015. 1 0.0 0.2
Blood in semen 1 0.0 0.2
Blood in semen fluid 1 0.0 0.2
Blood in semen. 1 0.0 0.2
Blood in the toilet after eliminating 1 0.0 0.2
Blood in urin 1 0.0 0.2
Blood in urine 8 0.1 1.9
Blood in urine (from light pink to straight blood). 1 0.0 0.2
Blood in urine. 2 0.0 0.5
Blood test 1 0.0 0.2
Blood test positive for cancer 1 0.0 0.2
Blood test. 2 0.0 0.5
Blood work colonoscopy. 1 0.0 0.2
bloody semen 1 0.0 0.2
bloody urine 1 0.0 0.2
Body scan MRI 1 0.0 0.2
Both brothers had been recently diagnosed 1 0.0 0.2
Brother diagnosed 1 0.0 0.2
Brother just diagnosed 1 0.0 0.2
Brother previously diagnosed 1 0.0 0.2
Burning sensation 1 0.0 0.2
Burning sensation in prostate area 1 0.0 0.2
Can’t remember. I had something done at the doctor’s office, I think. 1 0.0 0.2
Charles Briley, Sr. (Father) died stage 4 prostate cancer 1 0.0 0.2
Check up 1 0.0 0.2
Check up from new doctor. 1 0.0 0.2
Colon. 1 0.0 0.2
Complications from groin hernia surgery. 1 0.0 0.2
Could not hold urine 1 0.0 0.2
Couldn’t finish urinating 1 0.0 0.2
Couldn’t have sex 1 0.0 0.2
Cut in —- during surgery/cancer form infection 1 0.0 0.2
Did not have a clue. 1 0.0 0.2
Didn’t know I had it 1 0.0 0.2
Didn’t know! It was by mistake they found it. 1 0.0 0.2
Difficulty urinating 1 0.0 0.2
Digital exam showed no abnormality in size of prostate 1 0.0 0.2
Discomfort in the area 1 0.0 0.2
Doctor did PSA at exam 1 0.0 0.2
Doctor suggested evaluation because of my age 1 0.0 0.2
Doctor tested me and did not find anything, then tested me again PSA was high. 1 0.0 0.2
Doctor told me 1 0.0 0.2
Doctor, unusual count, not sure, ten years later found out. 1 0.0 0.2
Doctors check up 1 0.0 0.2
Doctors exam when I coughed 1 0.0 0.2
Don’t know 1 0.0 0.2
Don’t know. 1 0.0 0.2
Don’t know’ 1 0.0 0.2
Don’t remember 1 0.0 0.2
Dr. asked about family history of prostate cancer 1 0.0 0.2
During annual routine check. 1 0.0 0.2
ED 1 0.0 0.2
elevated PSA on life insurance test 1 0.0 0.2
Enlarge prostate 2 0.0 0.5
Enlarged prostate 3 0.1 0.7
Enlarged Prostate caused frequent trips to bathroom durning the night 1 0.0 0.2
Enlarged prostate surgery 1991 1 0.0 0.2
Enlarged prostrate. 1 0.0 0.2
ER dysfunction 1 0.0 0.2
Erection did not stay up to long 1 0.0 0.2
Examination. 1 0.0 0.2
Exercising walking leg start hurting. 1 0.0 0.2
Exposed to agent orange Vietnam June 1970 to May 1971 1 0.0 0.2
Extreme dizzy spell lead to doctor visit/diagnosis 1 0.0 0.2
Eye exam started the process of going to a doctor for 1st time in 25 years 1 0.0 0.2
Eye pain. Doctor told me to go to my GP (general practitioner). GP said/tested for prostate. 1 0.0 0.2
Family history 5 0.1 1.2
Family History 1 0.0 0.2
Family history. 1 0.0 0.2
Father had it. 1 0.0 0.2
Father had prostate cancer and died. 1 0.0 0.2
Fatigue 1 0.0 0.2
feeling having to urinate but wouldn’t actually go 1 0.0 0.2
Found during my yearly check up 1 0.0 0.2
Found during urinary problem, frequent urination surgery on prostate —- 1 0.0 0.2
Found out through kidney stone treatment 1 0.0 0.2
Frequent time urination 1 0.0 0.2
Frequent urination 4 0.1 1.0
Frequent urination at night during sleeping hours. 1 0.0 0.2
Frequent urination; no real symptoms. 1 0.0 0.2
Frequent urination. 2 0.0 0.5
Frequent urination. Told Dr. during regular physical. He prescribed medication (tablet) for it. At next physical asid urinating more often & asked to see Urologist. He agreed. Urologist did biopsy. 1 0.0 0.2
From a regular blood test. 1 0.0 0.2
General check up Dr. noticed raised abdomen 1 0.0 0.2
Get sick. 1 0.0 0.2
Got health insurance 1 0.0 0.2
Got very sick 1 0.0 0.2
Groin Pain 1 0.0 0.2
groin pain and discomfort 1 0.0 0.2
Gut feeling 1 0.0 0.2
Had a biopsy that came out positive. 1 0.0 0.2
Had a check up with doctor when I found out. 1 0.0 0.2
Had a slight fever and A UTI 1 0.0 0.2
Had an X-Ray done 1 0.0 0.2
Had bladder stones and an enlarged prostate. Note: Doctors at LSU Health Shreveport said it was the largest prostate they had ever seen before when I had it removed for prostate cancer in 2015. 1 0.0 0.2
Had blood in my urine. 1 0.0 0.2
Had blood in bowel went to see doctor 1 0.0 0.2
had blood test and PSA test was high 1 0.0 0.2
had cancer in kidney and further examinations indicated prostate cancer 1 0.0 0.2
Had fluctuating PSA 1 0.0 0.2
Had gotten very ill. 1 0.0 0.2
Had heat stroke went hospital and was sent to see my doctor. 1 0.0 0.2
had insanely intense and debilitating lower back pain…couldn’t walk or even move at times. Covered a span of 3 days before wife insisted I see a doctor. PSA test was taken at that time. 1 0.0 0.2
Had no idea 1 0.0 0.2
Had serious problems urinating. 1 0.0 0.2
Had swelling in leg, blood clot. 1 0.0 0.2
Had to make several trips to the restroom. 1 0.0 0.2
Had to urinate often 1 0.0 0.2
Had trauma-stomach surgery, blood work Oct. 2014 1 0.0 0.2
He did blood test to find out. 1 0.0 0.2
Heavy pain in my right kidney 1 0.0 0.2
Hereditary 1 0.0 0.2
high protein 1 0.0 0.2
High protein level in urine. 1 0.0 0.2
History of my father, made me self conscious at early age! 1 0.0 0.2
Hurt to urinate 1 0.0 0.2
I also had an enlarged prostate. 1 0.0 0.2
I also had rectal exam 1 0.0 0.2
I am not sure 1 0.0 0.2
I applied for ins. they did a PSA test, not my Dr. 1 0.0 0.2
I did a physical the first since I came to America in 2015 and my primary physician said my PSA needed further investigation. 1 0.0 0.2
I did not know. 1 0.0 0.2
I did test every 6 months missed 1 yr 6 months (my wife had cancer). 1 0.0 0.2
I felt something that felt like someone was moving their fingers across my abdomen as if they were trying to tickle me 1 0.0 0.2
I found blood in my urine. 1 0.0 0.2
I had a prostate infection 6 months prior to my diagnosis 1 0.0 0.2
I had a tingling feeling in my scrotum. 1 0.0 0.2
I had an enlarged prostate (urinating a lot), so from there it was diagnosed. 1 0.0 0.2
i had blood in my ejaculate 1 0.0 0.2
I had blood in my semen. 1 0.0 0.2
I had blood in my urine. 1 0.0 0.2
I had enlarged prostate during one of my routine check ups, my doctor found out that my PSA was high. 1 0.0 0.2
I had know clue. 1 0.0 0.2
I had lab work done my PSA was high 1 0.0 0.2
I had no clue 1 0.0 0.2
I had no indication. 1 0.0 0.2
I had no indications that I had prostate cancer. 1 0.0 0.2
I had no prior indications. I went to a health fair a couple of months after a routine physical and the #s were different, so after comparing the #s I was sent to a specialist. 1 0.0 0.2
I had no symptoms 1 0.0 0.2
I had no symptoms at all 1 0.0 0.2
I had no symptoms had a rectal exam and doctor felt an abnormality he suggested a biopsy 1 0.0 0.2
I had problems passing my urine. 1 0.0 0.2
I had prostrte surgery 1 0.0 0.2
I had to urinate every 45 minutes during an outing with a friend. 1 0.0 0.2
I had urinary 1 0.0 0.2
I just ask my doctor when do I see her. 1 0.0 0.2
I just thought I had bladder problems no cancer 1 0.0 0.2
I kept having to pee all the time. 1 0.0 0.2
I need to be tested. I knew that at age 35 and up. 1 0.0 0.2
I never had a PSA test done so she requested one. 1 0.0 0.2
I previously applied for insurance policy 1 0.0 0.2
I started having erectile disfunction 1 0.0 0.2
I started having urination issues, very frequent and hard to pass. 1 0.0 0.2
I told my primary doctor of my family history of Cancer 1 0.0 0.2
I took a blood test for a life inurance plicy and my PSA came back high. 1 0.0 0.2
I urinate a lot 1 0.0 0.2
I wanted a PSA test (me) 1 0.0 0.2
I wanted to buy life insurance the doctor tested me. 1 0.0 0.2
i was week and just not feeling myself. 1 0.0 0.2
I was admitted to the hospital because I was very sick that’s when they found out that I have cancer. 1 0.0 0.2
I was experiencing leakage of urine 1 0.0 0.2
I was having trouble urinating 1 0.0 0.2
I was in so much pain 1 0.0 0.2
i was speaking with a co-worker about his cancer experience 1 0.0 0.2
I was told to get checked once a year when I got 45 years old 1 0.0 0.2
I went for my exam. 1 0.0 0.2
Im good now 1 0.0 0.2
in the family of the men 1 0.0 0.2
Increased blood pressure during sex 1 0.0 0.2
Increased intensity lower back pain that I went to my doctor about 1 0.0 0.2
increased urinary frequency 1 0.0 0.2
Insurance questioned psa test results 1 0.0 0.2
Kept having to go to the bathroom 1 0.0 0.2
Kidney program tested my blood 1 0.0 0.2
kidney stone 1 0.0 0.2
Knew I felt different, but found out through my doctor. 1 0.0 0.2
Knowledge of family history and risk 1 0.0 0.2
Large prostate, frequent urination 1 0.0 0.2
Leg pain 1 0.0 0.2
Life ins. blood test 1 0.0 0.2
Loss of energy weight gain 1 0.0 0.2
Loss of weight 2 0.0 0.5
Lost 50 pounds fast 1 0.0 0.2
Lost a lot of weight 1 0.0 0.2
Low energy levels at times. 1 0.0 0.2
Low flow could not go to the bathroom 1 0.0 0.2
Low level fever fatigue. 1 0.0 0.2
Low testosterone levels 1 0.0 0.2
Lower back pain 1 0.0 0.2
Lumps found in colonoscopy 1 0.0 0.2
medical exam 1 0.0 0.2
MRI Test 1 0.0 0.2
MRI. 1 0.0 0.2
My 2 brothers, my grandfather and his brother had prostate cancer 1 0.0 0.2
My A1C was high and they wanted to test me 1 0.0 0.2
My blood PSA was high 1 0.0 0.2
My body was reacting abnormally for a period of time. 1 0.0 0.2
My brother developed prostate cancer/high PSA test 1 0.0 0.2
My brother got prostate cancer 1 0.0 0.2
my brother had prostate cancer 1 0.0 0.2
My brother had prostate cancer 1 0.0 0.2
My brother was diagnosed, so I got tested. I also had an enlarged prostate for years 1 0.0 0.2
My brothers had it. 1 0.0 0.2
My Doc. sent me for an exam because of my age. 1 0.0 0.2
My doctor did an exam and determined my prostate was enlarged. 1 0.0 0.2
My doctor gave me a test and seen the signs of it, Random check up. 1 0.0 0.2
My doctor was concerned with my enlarged prostate 1 0.0 0.2
My dog Jackson kept poking my groin with his nose there stare at me. 1 0.0 0.2
My dog sniffed and warned me 1 0.0 0.2
My family died of cancer. 1 0.0 0.2
My father and grandfather died from cancer. 1 0.0 0.2
My father died of prostate cancer. 1 0.0 0.2
My father had it, so I was tested. 1 0.0 0.2
My father told me to get checked because I wasn’t the age for it and because it runs in the men in his family. 1 0.0 0.2
My Father was diagnosed with Prostate Cancer 1 0.0 0.2
My father was diagnosed with prostate cancer. 1 0.0 0.2
My fathers diagnosis had me on alert 1 0.0 0.2
My glands were swollen. 1 0.0 0.2
My great uncle had it. I became aware and had check ups. 1 0.0 0.2
My life insurance company rejected me for life insurance 1 0.0 0.2
My new doctor asked if I ever seen a Urologist, I said no and he said I should. My PSA was 7. Had never had one. 1 0.0 0.2
my older brother was diagnosed and suggested I do the same. 1 0.0 0.2
My PCP found blood in my urine test. 1 0.0 0.2
My primary care physician referred me routinely. 1 0.0 0.2
my primary Dr suggested I see a Urologist 1 0.0 0.2
My PSA numbers 1 0.0 0.2
My PSA was being checked on my regular doctor visits. 1 0.0 0.2
my psa was between 8-10 1 0.0 0.2
My PSA was high. 1 0.0 0.2
My right testicle was swollen 1 0.0 0.2
My semen ejaculation decreased to almost none. 1 0.0 0.2
My sex life is over, no more sex. 1 0.0 0.2
My urologist wanted me to take a specific test for prostate cancer other than the PSA test 1 0.0 0.2
“NA” 1 0.0 0.2
Never 1 0.0 0.2
Never been tested in my sixty 1 0.0 0.2
New doctor noticed no PSA test with/Testosterone shots 1 0.0 0.2
No indication 1 0.0 0.2
No indication with regular check ups 1 0.0 0.2
No indications, no signs 1 0.0 0.2
No sign ever 1 0.0 0.2
No symptoms 1 0.0 0.2
No. 2 0.0 0.5
nodule in prostate 1 0.0 0.2
Nodule noticed on prostate during colonoscopy. 1 0.0 0.2
Nodules on groin 1 0.0 0.2
none 1 0.0 0.2
None 2 0.0 0.5
None I just had pain 1 0.0 0.2
NONE, doctor just had me take the PSA test. 1 0.0 0.2
Normal urological blood work showed-PSA 1 0.0 0.2
On 2-28-2016 10:15 AM PSA was 6354 (block) 1 0.0 0.2
On my double 18 wheeler truck accident, I had multiple internal injuries that is how in one of the test doctor said it looks like you may have —- 1 0.0 0.2
Other family members had prostate cancer 1 0.0 0.2
Over a time PSA was getting higher. 1 0.0 0.2
PA performed the finger test & referred me to an Urology Doctor 1 0.0 0.2
Pain in my groin. 1 0.0 0.2
Pain in my testicles 1 0.0 0.2
Pain in reproductive area 1 0.0 0.2
Pain in right leg, fearful did not know why. 1 0.0 0.2
Passing blood after sex. 1 0.0 0.2
PCA3 test 1 0.0 0.2
people’s have told me for my ages also because I was taking zantecs 1 0.0 0.2
peripheral vascular problems 1 0.0 0.2
PET Scan 1 0.0 0.2
Physical 1 0.0 0.2
Physical and finger up posterior 1 0.0 0.2
physical exam 1 0.0 0.2
Physical exam. 1 0.0 0.2
Physical Rectal Exam 1 0.0 0.2
Physical. 1 0.0 0.2
Pre Hernia surgery 1 0.0 0.2
Preparing for biopsy surgery and current psa was compared to 4 previous and found to be extremely high. 1 0.0 0.2
Primary doctor concern about result of blood work 1 0.0 0.2
Problem urinating 1 0.0 0.2
Procedure for bladder 1 0.0 0.2
prostate cancer were discovered during my urinary tract surgery. A cancer surgeon was called in to remove all microscopically visible cancer cells 1 0.0 0.2
Prostate study program at Henry Ford Hospital (1980’s). 1 0.0 0.2
Prostate swell 1 0.0 0.2
Prostate was large 1 0.0 0.2
PSA levels kept rising. 1 0.0 0.2
PSA reading was above normal not significantly higher than normal 1 0.0 0.2
PSA test 1 0.0 0.2
PSA testing and education program 1 0.0 0.2
PSA went from 14 to 45. 1 0.0 0.2
Rapid weight loss 1 0.0 0.2
Ref. by my medical doctor to see urologist. 1 0.0 0.2
Regular check up 2 0.0 0.5
Regular physical examination 1 0.0 0.2
Regular prostate check up felt a lump 1 0.0 0.2
Routine check-up. 1 0.0 0.2
Routine checkup by my primary care doctor 1 0.0 0.2
Routine exam 2 0.0 0.5
Routine Exam 1 0.0 0.2
Routine rectal exam 1 0.0 0.2
Scrotal pain of unknown origin 1 0.0 0.2
Semen in urine 1 0.0 0.2
Sexual issues 1 0.0 0.2
Sharp pain in right lower region. 1 0.0 0.2
Sharp pains in the groin area 1 0.0 0.2
Slip and fall exam. 1 0.0 0.2
Slow stream, but normal PSA 1 0.0 0.2
Something was discovered during routine health check up. 1 0.0 0.2
Stage 2 radiation it’s negative 1 0.0 0.2
Starting having erection problems 1 0.0 0.2
Strong family history 1 0.0 0.2
Strong Family History, Physician did not listen 1 0.0 0.2
Sweating 1 0.0 0.2
Swollen and tender to touch 1 0.0 0.2
Terrible family history. Mother had nine brothers, seven had prostate cancer. 1 0.0 0.2
Testicle pain 1 0.0 0.2
testing done 1 0.0 0.2
The Dr. just told me at a check up! I always wonder if that was a scam! 1 0.0 0.2
The nurse of Cosmos detected an abnormality during her test 1 0.0 0.2
There was traces of blood in my urine. 1 0.0 0.2
Things went good 1 0.0 0.2
Thought it was upset stomach and having pain. 1 0.0 0.2
To the DR for stomach pains 1 0.0 0.2
Tumor on right scrotum 1 0.0 0.2
TURP performed for LUTS. 1 0.0 0.2
Ultra sound of prostate 1 0.0 0.2
Unknown back pain 1 0.0 0.2
Up & down PSA 1 0.0 0.2
Urgency to urinate, Frequent urination 1 0.0 0.2
Urinary 1 0.0 0.2
Urinated frequently 1 0.0 0.2
Urinating slow 1 0.0 0.2
Urinating too much. 1 0.0 0.2
Vietnam was Agent Orange 1 0.0 0.2
Visible blood in ejaculate (once). 1 0.0 0.2
Waking up thru the night using the bathroom often 1 0.0 0.2
Weak urine stream, PSA of 13 on regular medical blood test 1 0.0 0.2
Weakness and loss of weight. 1 0.0 0.2
Weight loss 1 0.0 0.2
Went for normal lab test, blood in urine and came out then. 1 0.0 0.2
went for testing because insurance company required it 1 0.0 0.2
Went to doctor for check up 1 0.0 0.2
When I had my yearly physical 1 0.0 0.2
When VA check me 1 0.0 0.2
While in USAF, was a DaNang AB Vietnam mid 1965 1 0.0 0.2
With no actual symptoms 1 0.0 0.2
Yearly check-up. 1 0.0 0.2
NA 5427 92.9 NA
Total 5839 100.0 100.0

E2: Before diagnosis

  • E2. Before you were diagnosed with prostate cancer:
      1. Did you have any previous prostate biopsies that were negative?
      • 2=Yes
      • 1=No
      • 88=Don’t know
    • If yes, How many?
      • 1=1
      • 2=2
      • 3=3 or more
      1. Did you have any previous PSA blood tests that were considered normal?
      • 2=Yes
      • 1=No
      • 88=Don’t know
    • If yes, How many?
      • 1=1
      • 2=2
      • 3=3
      • 4=4
      • 5=5 or more
# 1
  e2aa <- as.factor(d[,"e2aa"])
# Make "*" to NA
e2aa[which(e2aa=="*")]<-"NA"
  levels(e2aa) <- list(Yes="2",
                      No="1",
                      Dont_know="88")
  e2aa <- ordered(e2aa, c("Yes","No","Dont_know"))
  
  new.d <- data.frame(new.d, e2aa)
  new.d <- apply_labels(new.d, e2aa = "prostate biopsies")
  temp.d <- data.frame (new.d, e2aa)  
  
  result<-questionr::freq(temp.d$e2aa,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "a. Did you have any previous prostate biopsies that were negative?")
a. Did you have any previous prostate biopsies that were negative?
n % val%
Yes 765 13.1 13.6
No 4282 73.3 76.2
Dont_know 576 9.9 10.2
NA 216 3.7 NA
Total 5839 100.0 100.0 
#2
  e2ab <- as.factor(d[,"e2ab"])
# Make "*" to NA
e2ab[which(e2ab=="*")]<-"NA"
  levels(e2ab) <- list(One="1",
                      Two="2",
                      Three_more="3")
  e2ab <- ordered(e2ab, c("One","Two","Three_more"))
  
  new.d <- data.frame(new.d, e2ab)
  new.d <- apply_labels(new.d, e2ab = "prostate biopsies_How many")
  temp.d <- data.frame (new.d, e2ab)  
  
  result<-questionr::freq(temp.d$e2ab,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "If yes, How many?")
If yes, How many?
n % val%
One 419 7.2 48.6
Two 234 4.0 27.1
Three_more 209 3.6 24.2
NA 4977 85.2 NA
Total 5839 100.0 100.0 
#3
  e2ba <- as.factor(d[,"e2ba"])
# Make "*" to NA
e2ba[which(e2ba=="*")]<-"NA"
  levels(e2ba) <- list(Yes="2",
                       No="1",
                       Dont_know="88")
  e2ba <- ordered(e2ba, c("Yes","No","Dont_know"))
  
  new.d <- data.frame(new.d, e2ba)
  new.d <- apply_labels(new.d, e2ba = "PSA blood tests")
  temp.d <- data.frame (new.d, e2ba)  
  
  result<-questionr::freq(temp.d$e2ba,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "b. Did you have any previous PSA blood tests that were considered normal?")
b. Did you have any previous PSA blood tests that were considered normal?
n % val%
Yes 2589 44.3 48.2
No 1423 24.4 26.5
Dont_know 1354 23.2 25.2
NA 473 8.1 NA
Total 5839 100.0 100.0 
#4
  e2bb <- as.factor(d[,"e2bb"])
  # Make "*" to NA
e2bb[which(e2bb=="*")]<-"NA"
  levels(e2bb) <- list(One="1",
                      Two="2",
                      Three="3",
                      Four="4",
                      Five_more="5")
  e2bb <- ordered(e2bb, c("One","Two","Threem","Four","Five_more"))
  
  new.d <- data.frame(new.d, e2bb)
  new.d <- apply_labels(new.d, e2bb = "PSA blood tests_how many")
  temp.d <- data.frame (new.d, e2bb)  
  
  result<-questionr::freq(temp.d$e2bb,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "If yes, How many?")
If yes, How many?
n % val%
One 264 4.5 12.0
Two 417 7.1 19.0
Threem 0 0.0 0.0
Four 275 4.7 12.5
Five_more 1241 21.3 56.5
NA 3642 62.4 NA
Total 5839 100.0 100.0

E3: Decision about PSA blood test

  • E3. Which of the following best describes your decision to have the PSA blood test that indicated that you had prostate cancer?
    • 1=I made the decision alone
    • 2=I made the decision together with a family member or friend
    • 3=I made the decision together with a family member or friend and my doctor, nurse, or health care provider
    • 4= I made the decision together with my doctor, nurse, or health care provider
    • 5=My doctor, nurse, or health care provider made the decision
    • 88=I do not know or remember how the decision was made
  e3 <- as.factor(d[,"e3"])
# Make "*" to NA
e3[which(e3=="*")]<-"NA"
  levels(e3) <- list(Alone="1",
                     With_family_or_friends="2",
                     With_family_and_doctor="3",
                     With_doctor="4",
                     Doctor_made="5",
                     Dont_know_or_remember="88")
  e3 <- ordered(e3, c("Alone","With_family_or_friends","With_family_and_doctor","With_doctor","Doctor_made","Dont_know_or_remember"))
  
  new.d <- data.frame(new.d, e3)
  new.d <- apply_labels(new.d, e3 = "decision to have the PSA blood test")
  temp.d <- data.frame (new.d, e3)  
  
  result<-questionr::freq(temp.d$e3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "E3")
E3
n % val%
Alone 861 14.7 15.4
With_family_or_friends 365 6.3 6.5
With_family_and_doctor 791 13.5 14.1
With_doctor 1519 26.0 27.1
Doctor_made 1737 29.7 31.0
Dont_know_or_remember 323 5.5 5.8
NA 243 4.2 NA
Total 5839 100.0 100.0

E4: Understanding of aggressiveness

  • E4. When you were diagnosed with prostate cancer, what was your understanding of how aggressive your cancer might be (i.e., how likely it was that your cancer might progress).
    • 1=Low risk of progression
    • 2=Intermediate risk of progression
    • 3=High risk of progression
    • 4=Unknown risk of progression
    • 88=Don’t know/Don’t remember
  e4 <- as.factor(d[,"e4"])
# Make "*" to NA
e4[which(e4=="*")]<-"NA"
  levels(e4) <- list(Low_risk="1",
                     Intermediate_risk="2",
                     High_risk="3",
                     Unknown_risk="4",
                     Dont_know_or_remember="88")
  e4 <- ordered(e4, c("Low_risk","Intermediate_risk","High_risk","Unknown_risk","Dont_know_or_remember"))
  
  new.d <- data.frame(new.d, e4)
  new.d <- apply_labels(new.d, e4 = "how aggressive")
  temp.d <- data.frame (new.d, e4)  
  
  result<-questionr::freq(temp.d$e4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e4")
e4
n % val%
Low_risk 2040 34.9 35.6
Intermediate_risk 1021 17.5 17.8
High_risk 1334 22.8 23.3
Unknown_risk 587 10.1 10.3
Dont_know_or_remember 743 12.7 13.0
NA 114 2.0 NA
Total 5839 100.0 100.0

E5: Gleason score

  • E5. What was your Gleason score when you were diagnosed with prostate cancer?
    • 1=6 or less
    • 2=7
    • 3=8-10
    • 88=Don’t know
  e5 <- as.factor(d[,"e5"])
# Make "*" to NA
e5[which(e5=="*")]<-"NA"
  levels(e5) <- list(Six_less="1",
                     Seven="2",
                     Eight_to_ten="3",
                     Dont_know="88")
  e5 <- ordered(e5, c("Six_less","Seven","Eight_to_ten","Dont_know"))
  
  new.d <- data.frame(new.d, e5)
  new.d <- apply_labels(new.d, e5 = "Gleason score")
  temp.d <- data.frame (new.d, e5)  
  
  result<-questionr::freq(temp.d$e5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e5")
e5
n % val%
Six_less 1145 19.6 20.3
Seven 1003 17.2 17.8
Eight_to_ten 856 14.7 15.2
Dont_know 2634 45.1 46.7
NA 201 3.4 NA
Total 5839 100.0 100.0

E6: Understanding of stage

  • E6. What was your understanding of the stage of your prostate cancer when you were diagnosed?
    • 1=Localized, confined to prostate
    • 2=Regional, tumor extended to regions around the prostate
    • 3=Distant, tumor extended to bones or other parts of body
    • 88=Don’t know about the stage
  e6 <- as.factor(d[,"e6"])
# Make "*" to NA
e6[which(e6=="*")]<-"NA"
  levels(e6) <- list(Localized="1",
                     Regional="2",
                     Distant="3",
                     Dont_know="88")
  e6 <- ordered(e6, c("Localized","Regional","Distant","Dont_know"))
  
  new.d <- data.frame(new.d, e6)
  new.d <- apply_labels(new.d, e6 = "Stage")
  temp.d <- data.frame (new.d, e6)  
  
  result<-questionr::freq(temp.d$e6,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e6")
e6
n % val%
Localized 4035 69.1 71.0
Regional 276 4.7 4.9
Distant 114 2.0 2.0
Dont_know 1256 21.5 22.1
NA 158 2.7 NA
Total 5839 100.0 100.0

E7: MRI guided biopsy

  • E7. Did you have a Magnetic Resonance Imaging (MRI)-guided biopsy to diagnose your cancer? (This is a different type of biopsy than the standard ultrasound biopsy that involves taking 12 random biopsy core samples. Instead, you would be placed in a large donut shaped machine that can be noisy. With assistance from the MRI, 2-3 targeted biopsies would be taken in areas of the tumor shown to be most aggressive.)
    • 2=Yes
    • 1=No
    • 88=Don’t Know
  e7 <- as.factor(d[,"e7"])
# Make "*" to NA
e7[which(e7=="*")]<-"NA"
  levels(e7) <- list(No="1",
                     Yes="2",
                     Dont_know="88")
  e7 <- ordered(e7, c("No","Yes","Dont_know"))
  
  new.d <- data.frame(new.d, e7)
  new.d <- apply_labels(new.d, e7 = "Stage")
  temp.d <- data.frame (new.d, e7)  
  
  result<-questionr::freq(temp.d$e7,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e7")
e7
n % val%
No 2202 37.7 38.9
Yes 2154 36.9 38.1
Dont_know 1304 22.3 23.0
NA 179 3.1 NA
Total 5839 100.0 100.0

E8: Decision about treatment

  • E8. How did you make your treatment decision?
    • 1=I made the decision alone
    • 2=I made the decision together with a family member or friend
    • 3=I made the decision together with a family member or friend and my doctor, nurse, or health care provider
    • 4=I made the decision together with my doctor, nurse, or health care provider
    • 5=My doctor , nurse, or health care provider made the decision
    • 6=I don’t know or remember how the decision was made
  e8 <- as.factor(d[,"e8"])
# Make "*" to NA
e8[which(e8=="*")]<-"NA"
  levels(e8) <- list(Alone="1",
                     With_family_or_friends="2",
                     With_family_and_doctor="3",
                     With_doctor="4",
                     Doctor_made="5",
                     Dont_know_or_remember="88")
  e8 <- ordered(e8, c("Alone","With_family_or_friends","With_family_and_doctor","With_doctor","Doctor_made","Dont_know_or_remember"))
  
  new.d <- data.frame(new.d, e8)
  new.d <- apply_labels(new.d, e8 = "treatment decision")
  temp.d <- data.frame (new.d, e8)  
  
  result<-questionr::freq(temp.d$e8,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e8")
e8
n % val%
Alone 796 13.6 14.4
With_family_or_friends 834 14.3 15.1
With_family_and_doctor 2069 35.4 37.3
With_doctor 1349 23.1 24.3
Doctor_made 493 8.4 8.9
Dont_know_or_remember 0 0.0 0.0
NA 298 5.1 NA
Total 5839 100.0 100.0

E9: The most important factors of tx

  • E9. What were the most important factors you considered in making your treatment decision? Mark all that apply.
    • E9_1: 1=Best chance for cure of my cancer
    • E9_2: 1=Minimize side effects related to sexual function
    • E9_3: 1=Minimize side effects related to urinary function
    • E9_4: 1=Minimize side effects related to bowel function
    • E9_5: 1=Minimize financial cost
    • E9_6: 1=Amount of time and travel required to receive treatments
    • E9_7: 1=Length of recovery time
    • E9_8: 1=Amount of time away from work
    • E9_9: 1=Burden on family members
    • E9_10: 1=Reduce worry and concern about cancer
  e9_1 <- as.factor(d[,"e9_1"])
  levels(e9_1) <- list(Best_for_cure="1")
  new.d <- data.frame(new.d, e9_1)
  new.d <- apply_labels(new.d, e9_1 = "Best for cure")
  temp.d <- data.frame (new.d, e9_1)  
  result<-questionr::freq(temp.d$e9_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Best chance for cure of my cancer")
1. Best chance for cure of my cancer
n % val%
Best_for_cure 5010 85.8 100
NA 829 14.2 NA
Total 5839 100.0 100 
  e9_2 <- as.factor(d[,"e9_2"])
  levels(e9_2) <- list(side_effects_sexual="1")
  new.d <- data.frame(new.d, e9_2)
  new.d <- apply_labels(new.d, e9_2 = "side effects sexual")
  temp.d <- data.frame (new.d, e9_2)  
  result<-questionr::freq(temp.d$e9_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Minimize side effects related to sexual function")
2. Minimize side effects related to sexual function
n % val%
side_effects_sexual 1754 30 100
NA 4085 70 NA
Total 5839 100 100 
  e9_3 <- as.factor(d[,"e9_3"])
  levels(e9_3) <- list(side_effects_urinary="1")
  new.d <- data.frame(new.d, e9_3)
  new.d <- apply_labels(new.d, e9_3 = "side effects urinary")
  temp.d <- data.frame (new.d, e9_3)  
  result<-questionr::freq(temp.d$e9_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Minimize side effects related to urinary function")
3. Minimize side effects related to urinary function
n % val%
side_effects_urinary 1548 26.5 100
NA 4291 73.5 NA
Total 5839 100.0 100 
  e9_4 <- as.factor(d[,"e9_4"])
  levels(e9_4) <- list(side_effects_bowel="1")
  new.d <- data.frame(new.d, e9_4)
  new.d <- apply_labels(new.d, e9_4 = "side effects bowel")
  temp.d <- data.frame (new.d, e9_4)  
  result<-questionr::freq(temp.d$e9_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "4. Minimize side effects related to bowel function")
4. Minimize side effects related to bowel function
n % val%
side_effects_bowel 770 13.2 100
NA 5069 86.8 NA
Total 5839 100.0 100 
  e9_5 <- as.factor(d[,"e9_5"])
  levels(e9_5) <- list(financial_cost="1")
  new.d <- data.frame(new.d, e9_5)
  new.d <- apply_labels(new.d, e9_5 = "financial cost")
  temp.d <- data.frame (new.d, e9_5)  
  result<-questionr::freq(temp.d$e9_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "5. Minimize financial cost")
5. Minimize financial cost
n % val%
financial_cost 348 6 100
NA 5491 94 NA
Total 5839 100 100 
  e9_6 <- as.factor(d[,"e9_6"])
  levels(e9_6) <- list(time_and_travel="1")
  new.d <- data.frame(new.d, e9_6)
  new.d <- apply_labels(new.d, e9_6 = "time and travel")
  temp.d <- data.frame (new.d, e9_6)  
  result<-questionr::freq(temp.d$e9_6,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "6. Amount of time and travel required to receive treatments")
6. Amount of time and travel required to receive treatments
n % val%
time_and_travel 652 11.2 100
NA 5187 88.8 NA
Total 5839 100.0 100 
  e9_7 <- as.factor(d[,"e9_7"])
  levels(e9_7) <- list(recovery_time="1")
  new.d <- data.frame(new.d, e9_7)
  new.d <- apply_labels(new.d, e9_7 = "recovery time")
  temp.d <- data.frame (new.d, e9_7)  
  result<-questionr::freq(temp.d$e9_7,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "7. Length of recovery time")
7. Length of recovery time
n % val%
recovery_time 1134 19.4 100
NA 4705 80.6 NA
Total 5839 100.0 100 
  e9_8 <- as.factor(d[,"e9_8"])
  levels(e9_8) <- list(time_away_from_work="1")
  new.d <- data.frame(new.d, e9_8)
  new.d <- apply_labels(new.d, e9_8 = "time away from work")
  temp.d <- data.frame (new.d, e9_8)  
  result<-questionr::freq(temp.d$e9_8,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "8. Amount of time away from work")
8. Amount of time away from work
n % val%
time_away_from_work 475 8.1 100
NA 5364 91.9 NA
Total 5839 100.0 100 
  e9_9 <- as.factor(d[,"e9_9"])
  levels(e9_9) <- list(family_burden="1")
  new.d <- data.frame(new.d, e9_9)
  new.d <- apply_labels(new.d, e9_9 = "family burden")
  temp.d <- data.frame (new.d, e9_9)  
  result<-questionr::freq(temp.d$e9_9,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "9. Burden on family members")
9. Burden on family members
n % val%
family_burden 856 14.7 100
NA 4983 85.3 NA
Total 5839 100.0 100 
  e9_10 <- as.factor(d[,"e9_10"])
  levels(e9_10) <- list(Reduce_worry_concern="1")
  new.d <- data.frame(new.d, e9_10)
  new.d <- apply_labels(new.d, e9_10 = "Reduce worry and concern")
  temp.d <- data.frame (new.d, e9_10)  
  result<-questionr::freq(temp.d$e9_10,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "10. Reduce worry and concern about cancer")
10. Reduce worry and concern about cancer
n % val%
Reduce_worry_concern 2519 43.1 100
NA 3320 56.9 NA
Total 5839 100.0 100

E10: Recieved treatment

  • E10. Please mark all the treatments that you have received for your prostate cancer? Mark all that apply.
    • E10_1: 1=Haven’t had any treatment yet (and not specifically on active surveillance or watchful waiting).
    • E10_2: 1=Active Surveillance or watchful waiting
    • E10_3: 1=Prostate surgery (prostatectomy)
    • E10_4: 1=Radiation to the prostate
    • E10_5: 1=Hormonal treatments
    • E10_6: 1=Provenge/immunotherapy (Sipuleucel T)
    • E10_7: 1=Chemotherapy (docetaxel, cabazitaxel, other chemotherapy)
    • E10_8: 1=Other treatments to the prostate (HIFU (High Intensity Focused Ultrasound), RFA (Radio Frequency Ablation), laser, focal therapy, cryotherapy (freezing of the prostate))
  e10_1 <- as.factor(d[,"e10_1"])
  levels(e10_1) <- list(no_treatment="1")
  new.d <- data.frame(new.d, e10_1)
  new.d <- apply_labels(new.d, e10_1 = "no treatment")
  temp.d <- data.frame (new.d, e10_1)  
  result<-questionr::freq(temp.d$e10_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Haven’t had any treatment  yet (and not specifically on active surveillance or watchful waiting).")
1. Haven’t had any treatment yet (and not specifically on active surveillance or watchful waiting).
n % val%
no_treatment 327 5.6 100
NA 5512 94.4 NA
Total 5839 100.0 100 
  e10_2 <- as.factor(d[,"e10_2"])
  levels(e10_2) <- list(Active_Surveillance="1")
  new.d <- data.frame(new.d, e10_2)
  new.d <- apply_labels(new.d, e10_2 = "Active Surveillance")
  temp.d <- data.frame (new.d, e10_2)  
  result<-questionr::freq(temp.d$e10_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Active Surveillance or watchful waiting")
2. Active Surveillance or watchful waiting
n % val%
Active_Surveillance 964 16.5 100
NA 4875 83.5 NA
Total 5839 100.0 100 
  e10_3 <- as.factor(d[,"e10_3"])
  levels(e10_3) <- list(prostatectomy="1")
  new.d <- data.frame(new.d, e10_3)
  new.d <- apply_labels(new.d, e10_3 = "prostatectomy")
  temp.d <- data.frame (new.d, e10_3)  
  result<-questionr::freq(temp.d$e10_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Prostate surgery (prostatectomy)")
3. Prostate surgery (prostatectomy)
n % val%
prostatectomy 1927 33 100
NA 3912 67 NA
Total 5839 100 100 
  e10_4 <- as.factor(d[,"e10_4"])
  levels(e10_4) <- list(Radiation="1")
  new.d <- data.frame(new.d, e10_4)
  new.d <- apply_labels(new.d, e10_4 = "Radiation")
  temp.d <- data.frame (new.d, e10_4)  
  result<-questionr::freq(temp.d$e10_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "4. Radiation to the prostate")
4. Radiation to the prostate
n % val%
Radiation 2313 39.6 100
NA 3526 60.4 NA
Total 5839 100.0 100 
  e10_5 <- as.factor(d[,"e10_5"])
  levels(e10_5) <- list(Hormonal_treatments="1")
  new.d <- data.frame(new.d, e10_5)
  new.d <- apply_labels(new.d, e10_5 = "Hormonal treatments")
  temp.d <- data.frame (new.d, e10_5)  
  result<-questionr::freq(temp.d$e10_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "5. Hormonal treatments")
5. Hormonal treatments
n % val%
Hormonal_treatments 792 13.6 100
NA 5047 86.4 NA
Total 5839 100.0 100 
  e10_6 <- as.factor(d[,"e10_6"])
  levels(e10_6) <- list(Provenge_immunotherapy="1")
  new.d <- data.frame(new.d, e10_6)
  new.d <- apply_labels(new.d, e10_6 = "Provenge immunotherapy")
  temp.d <- data.frame (new.d, e10_6)  
  result<-questionr::freq(temp.d$e10_6,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "6. Provenge/immunotherapy (Sipuleucel T)")
6. Provenge/immunotherapy (Sipuleucel T)
n % val%
Provenge_immunotherapy 59 1 100
NA 5780 99 NA
Total 5839 100 100 
  e10_7 <- as.factor(d[,"e10_7"])
  levels(e10_7) <- list(Chemotherapy="1")
  new.d <- data.frame(new.d, e10_7)
  new.d <- apply_labels(new.d, e10_7 = "Chemotherapy")
  temp.d <- data.frame (new.d, e10_7)  
  result<-questionr::freq(temp.d$e10_7,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "7. Chemotherapy (docetaxel, cabazitaxel, other chemotherapy)")
7. Chemotherapy (docetaxel, cabazitaxel, other chemotherapy)
n % val%
Chemotherapy 184 3.2 100
NA 5655 96.8 NA
Total 5839 100.0 100 
  e10_8 <- as.factor(d[,"e10_8"])
  levels(e10_8) <- list(Other="1")
  new.d <- data.frame(new.d, e10_8)
  new.d <- apply_labels(new.d, e10_8 = "Other")
  temp.d <- data.frame (new.d, e10_8)  
  result<-questionr::freq(temp.d$e10_8,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "8. Other treatments to the prostate ")
8. Other treatments to the prostate
n % val%
Other 274 4.7 100
NA 5565 95.3 NA
Total 5839 100.0 100

E10-3 Prostatectomy

  • E10_3. Prostate surgery (prostatectomy), indicate which type(s):
    • E10_3_1: 1=Robotic or laproscopic surgery resulting in removal of the prostate
    • E10_3_2: 1=Open surgical removal of the prostate (using a long incision)
    • E10_3_3: 1=Had surgery but unsure of type
  e10_3_1 <- as.factor(d[,"e10_3_1"])
  levels(e10_3_1) <- list(Robotic_laproscopic_surgery="1")
  new.d <- data.frame(new.d, e10_3_1)
  new.d <- apply_labels(new.d, e10_3_1 = "Robotic or laproscopic surgery")
  temp.d <- data.frame (new.d, e10_3_1)  
  result<-questionr::freq(temp.d$e10_3_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Robotic or laproscopic surgery resulting in removal of the prostate")
1. Robotic or laproscopic surgery resulting in removal of the prostate
n % val%
Robotic_laproscopic_surgery 1979 33.9 100
NA 3860 66.1 NA
Total 5839 100.0 100 
  e10_3_2 <- as.factor(d[,"e10_3_2"])
  levels(e10_3_2) <- list(Open_surgical_removal="1")
  new.d <- data.frame(new.d, e10_3_2)
  new.d <- apply_labels(new.d, e10_3_2 = "Open surgical removal")
  temp.d <- data.frame (new.d, e10_3_2)  
  result<-questionr::freq(temp.d$e10_3_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Open surgical removal of the prostate (using a long incision)")
2. Open surgical removal of the prostate (using a long incision)
n % val%
Open_surgical_removal 302 5.2 100
NA 5537 94.8 NA
Total 5839 100.0 100 
  e10_3_3 <- as.factor(d[,"e10_3_3"])
  levels(e10_3_3) <- list(unsure_of_type="1")
  new.d <- data.frame(new.d, e10_3_3)
  new.d <- apply_labels(new.d, e10_3_3 = "unsure of type")
  temp.d <- data.frame (new.d, e10_3_3)  
  result<-questionr::freq(temp.d$e10_3_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Had surgery but unsure of type")
3. Had surgery but unsure of type
n % val%
unsure_of_type 347 5.9 100
NA 5492 94.1 NA
Total 5839 100.0 100

E10-4 Radiation

  • E10_4. Radiation to the prostate, indicate which type(s):
    • E10_4_1: 1=External beam radiation, where beams are aimed from the outside of your body (including IMRT (Intensity Modulated Radiation Therapy), IGRT (Image-Guided Radiation Therapy), arc therapy, proton beam, cyberknife, or 3D-conformal beam therapy)
    • E10_4_2: 1 = Insertion of radiation seed/roods (brachytherapy)
    • E10_4_3: 1=Other types of radiation therapy, or unsure of what type
  e10_4_1 <- as.factor(d[,"e10_4_1"])
  levels(e10_4_1) <- list(External_beam_radiation="1")
  new.d <- data.frame(new.d, e10_4_1)
  new.d <- apply_labels(new.d, e10_4_1 = "External beam radiation")
  temp.d <- data.frame (new.d, e10_4_1)  
  result<-questionr::freq(temp.d$e10_4_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. External beam radiation")
1. External beam radiation
n % val%
External_beam_radiation 2147 36.8 100
NA 3692 63.2 NA
Total 5839 100.0 100 
  e10_4_2 <- as.factor(d[,"e10_4_2"])
  levels(e10_4_2) <- list(brachytherapy="1")
  new.d <- data.frame(new.d, e10_4_2)
  new.d <- apply_labels(new.d, e10_4_2 = "brachytherapy")
  temp.d <- data.frame (new.d, e10_4_2)  
  result<-questionr::freq(temp.d$e10_4_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. brachytherapy")
2. brachytherapy
n % val%
brachytherapy 911 15.6 100
NA 4928 84.4 NA
Total 5839 100.0 100 
  e10_4_3 <- as.factor(d[,"e10_4_3"])
  levels(e10_4_3) <- list(Other_types="1")
  new.d <- data.frame(new.d, e10_4_3)
  new.d <- apply_labels(new.d, e10_4_3 = "Other types")
  temp.d <- data.frame (new.d, e10_4_3)  
  result<-questionr::freq(temp.d$e10_4_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Other types")
3. Other types
n % val%
Other_types 326 5.6 100
NA 5513 94.4 NA
Total 5839 100.0 100

E10-5 Hormonal treatments

  • E10_5. Hormonal treatments, indicate which type(s):
    • E10_5_1: 1=Hormone shots (Lupron, Zoladex, Firmagon, Eligard, Vantas)
    • E10_5_2: 1= Surgical removal of testicles (orchiectomy)
    • E10_5_3: 1=Casodex (bicalutamide) or Eulexin (flutamide) pills
    • E10_5_4: 1=Zytiga (abiraterone) or Xtandi (enzalutamide) pills
    • E10_5_5: 1=Had hormone treatment, but unsure of type
  e10_5_1 <- as.factor(d[,"e10_5_1"])
  levels(e10_5_1) <- list(Hormone_shots="1")
  new.d <- data.frame(new.d, e10_5_1)
  new.d <- apply_labels(new.d, e10_5_1 = "Hormone shots")
  temp.d <- data.frame (new.d, e10_5_1)  
  result<-questionr::freq(temp.d$e10_5_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Hormone shots")
1. Hormone shots
n % val%
Hormone_shots 1106 18.9 100
NA 4733 81.1 NA
Total 5839 100.0 100 
  e10_5_2 <- as.factor(d[,"e10_5_2"])
  levels(e10_5_2) <- list(orchiectomy="1")
  new.d <- data.frame(new.d, e10_5_2)
  new.d <- apply_labels(new.d, e10_5_2 = "orchiectomy")
  temp.d <- data.frame (new.d, e10_5_2)  
  result<-questionr::freq(temp.d$e10_5_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. orchiectomy")
2. orchiectomy
n % val%
orchiectomy 64 1.1 100
NA 5775 98.9 NA
Total 5839 100.0 100 
  e10_5_3 <- as.factor(d[,"e10_5_3"])
  levels(e10_5_3) <- list(Casodex_Eulexin="1")
  new.d <- data.frame(new.d, e10_5_3)
  new.d <- apply_labels(new.d, e10_5_3 = "Casodex or Eulexin pills")
  temp.d <- data.frame (new.d, e10_5_3)  
  result<-questionr::freq(temp.d$e10_5_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Casodex or Eulexin pills")
3. Casodex or Eulexin pills
n % val%
Casodex_Eulexin 128 2.2 100
NA 5711 97.8 NA
Total 5839 100.0 100 
  e10_5_4 <- as.factor(d[,"e10_5_4"])
  levels(e10_5_4) <- list(Zytiga_Xtandi="1")
  new.d <- data.frame(new.d, e10_5_4)
  new.d <- apply_labels(new.d, e10_5_4 = "Zytiga or Xtandi pills")
  temp.d <- data.frame (new.d, e10_5_4)  
  result<-questionr::freq(temp.d$e10_5_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "4. Zytiga or Xtandi pills")
4. Zytiga or Xtandi pills
n % val%
Zytiga_Xtandi 112 1.9 100
NA 5727 98.1 NA
Total 5839 100.0 100 
  e10_5_5 <- as.factor(d[,"e10_5_5"])
  levels(e10_5_5) <- list(unsure_type="1")
  new.d <- data.frame(new.d, e10_5_5)
  new.d <- apply_labels(new.d, e10_5_5 = "unsure of type")
  temp.d <- data.frame (new.d, e10_5_5)  
  result<-questionr::freq(temp.d$e10_5_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "5. unsure of type")
5. unsure of type
n % val%
unsure_type 286 4.9 100
NA 5553 95.1 NA
Total 5839 100.0 100

E11: Treatment decision

  • E11. Your treatment decision: How true is each of the following statements for you?
      1. I had all the information I needed when a treatment was chosen for my prostate cancer
      1. My doctors told me the whole story about the effects of treatment
      1. I knew the right questions to ask my doctor
      1. I had enough time to make a decision about my treatment
      1. I am satisfied with the choices I made in treating my prostate cancer
      1. I would recommend the treatment I had to a close relative or friend
      • 1=Not at all
      • 2=A little bit
      • 3=Somewhat
      • 4=Quite a bit
      • 5=Very much
  e11a <- as.factor(d[,"e11a"])
# Make "*" to NA
e11a[which(e11a=="*")]<-"NA"
  levels(e11a) <- list(Not_at_all="1",
                       A_little_bit="2",
                       Somewhat="3",
                       Quite_a_bit="4",
                       Very_much="5")
  new.d <- data.frame(new.d, e11a)
  new.d <- apply_labels(new.d, e11a = "all info")
  temp.d <- data.frame (new.d, e11a)  
  result<-questionr::freq(temp.d$e11a,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "a. I had all the information I needed when a treatment was chosen for my prostate cancer")
a. I had all the information I needed when a treatment was chosen for my prostate cancer
n % val%
Not_at_all 181 3.1 3.2
A_little_bit 204 3.5 3.6
Somewhat 648 11.1 11.6
Quite_a_bit 1483 25.4 26.5
Very_much 3078 52.7 55.0
NA 245 4.2 NA
Total 5839 100.0 100.0 
  e11b <- as.factor(d[,"e11b"])
# Make "*" to NA
e11b[which(e11b=="*")]<-"NA"
  levels(e11b) <- list(Not_at_all="1",
                       A_little_bit="2",
                       Somewhat="3",
                       Quite_a_bit="4",
                       Very_much="5")
  new.d <- data.frame(new.d, e11b)
  new.d <- apply_labels(new.d, e11b = "be told about effects")
  temp.d <- data.frame (new.d, e11b)  
  result<-questionr::freq(temp.d$e11b,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "b. My doctors told me the whole story about the effects of treatment")
b. My doctors told me the whole story about the effects of treatment
n % val%
Not_at_all 153 2.6 2.7
A_little_bit 216 3.7 3.9
Somewhat 662 11.3 11.8
Quite_a_bit 1376 23.6 24.5
Very_much 3201 54.8 57.1
NA 231 4.0 NA
Total 5839 100.0 100.0 
  e11c <- as.factor(d[,"e11c"])
  # Make "*" to NA
e11c[which(e11c=="*")]<-"NA"
  levels(e11c) <- list(Not_at_all="1",
                       A_little_bit="2",
                       Somewhat="3",
                       Quite_a_bit="4",
                       Very_much="5")
  new.d <- data.frame(new.d, e11c)
  new.d <- apply_labels(new.d, e11c = "right questions to ask")
  temp.d <- data.frame (new.d, e11c)  
  result<-questionr::freq(temp.d$e11c,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "c. I knew the right questions to ask my doctor")
c. I knew the right questions to ask my doctor
n % val%
Not_at_all 554 9.5 9.9
A_little_bit 656 11.2 11.7
Somewhat 1797 30.8 32.2
Quite_a_bit 1162 19.9 20.8
Very_much 1419 24.3 25.4
NA 251 4.3 NA
Total 5839 100.0 100.0 
  e11d <- as.factor(d[,"e11d"])
  # Make "*" to NA
e11d[which(e11d=="*")]<-"NA"
  levels(e11d) <- list(Not_at_all="1",
                       A_little_bit="2",
                       Somewhat="3",
                       Quite_a_bit="4",
                       Very_much="5")
  new.d <- data.frame(new.d, e11d)
  new.d <- apply_labels(new.d, e11d = "enough time to decide")
  temp.d <- data.frame (new.d, e11d)  
  result<-questionr::freq(temp.d$e11d,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "d. I had enough time to make a decision about my treatment")
d. I had enough time to make a decision about my treatment
n % val%
Not_at_all 165 2.8 3.0
A_little_bit 224 3.8 4.0
Somewhat 845 14.5 15.1
Quite_a_bit 1361 23.3 24.4
Very_much 2994 51.3 53.6
NA 250 4.3 NA
Total 5839 100.0 100.0 
  e11e <- as.factor(d[,"e11e"])
  # Make "*" to NA
e11e[which(e11e=="*")]<-"NA"
  levels(e11e) <- list(Not_at_all="1",
                       A_little_bit="2",
                       Somewhat="3",
                       Quite_a_bit="4",
                       Very_much="5")
  new.d <- data.frame(new.d, e11e)
  new.d <- apply_labels(new.d, e11e = "satisfied with the choices")
  temp.d <- data.frame (new.d, e11e)  
  result<-questionr::freq(temp.d$e11e,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e. I am satisfied with the choices I made in treating my prostate cancer")
e. I am satisfied with the choices I made in treating my prostate cancer
n % val%
Not_at_all 213 3.6 3.8
A_little_bit 225 3.9 4.0
Somewhat 689 11.8 12.3
Quite_a_bit 867 14.8 15.5
Very_much 3611 61.8 64.4
NA 234 4.0 NA
Total 5839 100.0 100.0 
  e11f <- as.factor(d[,"e11f"])
  # Make "*" to NA
e11f[which(e11f=="*")]<-"NA"
  levels(e11f) <- list(Not_at_all="1",
                       A_little_bit="2",
                       Somewhat="3",
                       Quite_a_bit="4",
                       Very_much="5")
  new.d <- data.frame(new.d, e11f)
  new.d <- apply_labels(new.d, e11f = "would recommend")
  temp.d <- data.frame (new.d, e11f)  
  result<-questionr::freq(temp.d$e11f,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f. I would recommend the treatment I had to a close relative or friend")
f. I would recommend the treatment I had to a close relative or friend
n % val%
Not_at_all 341 5.8 6.1
A_little_bit 227 3.9 4.1
Somewhat 803 13.8 14.4
Quite_a_bit 767 13.1 13.8
Very_much 3424 58.6 61.6
NA 277 4.7 NA
Total 5839 100.0 100.0

E12: Instructions from doctors or nurses

  • E12. Have you ever received instructions from a doctor, nurse, or other health professional about who you should see for routine prostate cancer checkups or monitoring?
    • 2=Yes
    • 1=No
    • 88=Don’t Know/not sure
  e12 <- as.factor(d[,"e12"])
# Make "*" to NA
e12[which(e12=="*")]<-"NA"
  levels(e12) <- list(No="1",
                     Yes="2",
                     Dont_know="88")
  e12 <- ordered(e12, c("No","Yes","Dont_know"))
  
  new.d <- data.frame(new.d, e12)
  new.d <- apply_labels(new.d, e12 = "received instructions")
  temp.d <- data.frame (new.d, e12)  
  
  result<-questionr::freq(temp.d$e12,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e12")
e12
n % val%
No 697 11.9 12.3
Yes 4662 79.8 82.5
Dont_know 295 5.1 5.2
NA 185 3.2 NA
Total 5839 100.0 100.0

E13: # of PSA blood test

  • E13. Since your prostate cancer diagnosis, how many times have you had a PSA blood test?
    • 0=None
    • 1=1
    • 2=2
    • 3=3
    • 4=4 or more
    • 88=Don’t know/not sure
  e13 <- as.factor(d[,"e13"])
# Make "*" to NA
e13[which(e13=="*")]<-"NA"
  levels(e13) <- list(None="0",
                      One="1",
                      Two="2",
                     Three="3",
                     Four_more="4",
                     Dont_know="88")
  e13 <- ordered(e13, c("None","One","Two","Three","Four_more","Dont_know"))
  
  new.d <- data.frame(new.d, e13)
  new.d <- apply_labels(new.d, e13 = "times of PSA blood test")
  temp.d <- data.frame (new.d, e13)  
  
  result<-questionr::freq(temp.d$e13,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e13")
e13
n % val%
None 133 2.3 2.3
One 146 2.5 2.6
Two 302 5.2 5.3
Three 549 9.4 9.7
Four_more 4020 68.8 71.0
Dont_know 510 8.7 9.0
NA 179 3.1 NA
Total 5839 100.0 100.0

E14: Be told PSA was rising

  • E14. Since diagnosis or treatment, have you ever been told that your PSA was rising?
    • 2=Yes
    • 1=No
    • 88=Don’t Know/not sure
  e14 <- as.factor(d[,"e14"])
# Make "*" to NA
e14[which(e14=="*")]<-"NA"
  levels(e14) <- list(No="1",
                     Yes="2",
                     Dont_know="88")
  e14 <- ordered(e14, c("No","Yes","Dont_know"))
  
  new.d <- data.frame(new.d, e14)
  new.d <- apply_labels(new.d, e14 = "been told PSA was rising")
  temp.d <- data.frame (new.d, e14)  
  
  result<-questionr::freq(temp.d$e14,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e14")
e14
n % val%
No 4076 69.8 72.0
Yes 1240 21.2 21.9
Dont_know 345 5.9 6.1
NA 178 3.0 NA
Total 5839 100.0 100.0

E15: Recurred or got worse

  • E15. Since you were diagnosed, did your doctor ever tell you that your prostate cancer came back (recurred) or progressed (got worse)?
    • 2=Yes
    • 1=No
    • 88=Don’t Know/not sure
  e15 <- as.factor(d[,"e15"])
# Make "*" to NA
e15[which(e15=="*")]<-"NA"
  levels(e15) <- list(No="1",
                     Yes="2",
                     Dont_know="88")
  e15 <- ordered(e15, c("No","Yes","Dont_know"))
  
  new.d <- data.frame(new.d, e15)
  new.d <- apply_labels(new.d, e15 = "been told recurred progressed")
  temp.d <- data.frame (new.d, e15)  
  
  result<-questionr::freq(temp.d$e15,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e15")
e15
n % val%
No 4919 84.2 86.8
Yes 502 8.6 8.9
Dont_know 249 4.3 4.4
NA 169 2.9 NA
Total 5839 100.0 100.0

F1: Height

  • F1. How tall are you?
  f1cm <- d[,"f1cm"]
 
  new.d <- data.frame(new.d, f1cm)
  new.d <- apply_labels(new.d, f1cm = "height in cm")
  temp.d <- data.frame (new.d, f1cm)  
  
  result<-questionr::freq(temp.d$f1cm,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "How tall are you? (cm)")
How tall are you? (cm)
n % val%
0 10 0.2 8.8
0 0 1 0.0 0.9
0 9 1 0.0 0.9
1 13 0.2 11.4
10 4 0.1 3.5
11 5 0.1 4.4
111 1 0.0 0.9
12 1 0.0 0.9
135 1 0.0 0.9
148 1 0.0 0.9
151 1 0.0 0.9
152 1 0.0 0.9
154 1 0.0 0.9
164 1 0.0 0.9
165 2 0.0 1.8
17 1 0.0 0.9
170 1 0.0 0.9
173 1 0.0 0.9
175 1 0.0 0.9
177 1 0.0 0.9
178 2 0.0 1.8
180 2 0.0 1.8
181 2 0.0 1.8
185 2 0.0 1.8
188 1 0.0 0.9
190 4 0.1 3.5
2 7 0.1 6.1
2** 1 0.0 0.9
200 1 0.0 0.9
21 1 0.0 0.9
211 1 0.0 0.9
215 1 0.0 0.9
225 1 0.0 0.9
228 1 0.0 0.9
230 1 0.0 0.9
234 1 0.0 0.9
245 1 0.0 0.9
247 1 0.0 0.9
250 1 0.0 0.9
255 1 0.0 0.9
265 1 0.0 0.9
280 1 0.0 0.9
290 1 0.0 0.9
3 2 0.0 1.8
44 1 0.0 0.9
47 1 0.0 0.9
5 6 0.1 5.3
6 2 0.0 1.8
7 4 0.1 3.5
72 1 0.0 0.9
78 1 0.0 0.9
8 3 0.1 2.6
9 7 0.1 6.1
91 1 0.0 0.9
NA 5725 98.0 NA
Total 5839 100.0 100.0

F2: Weight

  • F2. How much do you current weight?
  f2lbs <- d[,"f2lbs"]
  new.d <- data.frame(new.d, f2lbs)
  new.d <- apply_labels(new.d, f2lbs = "weight in lbs")
  temp.d <- data.frame (new.d, f2lbs)  
  result<-questionr::freq(temp.d$f2lbs,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "How much do you current weight? (lbs)")
How much do you current weight? (lbs)
n % val%
* 4 0.1 0.1
* 0 1 0.0 0.0
* 3 1 0.0 0.0
* 4 1 0.0 0.0
* 5 2 0.0 0.0
* 9 1 0.0 0.0
*0 2 0.0 0.0
*2 1 0.0 0.0
*35 1 0.0 0.0
*4 2 0.0 0.0
*5 2 0.0 0.0
*6 1 0.0 0.0
*8 1 0.0 0.0
*99 1 0.0 0.0
0 2 0.0 0.0
0* 1 0.0 0.0
1 12 0.2 0.2
1 * 4 0.1 0.1
1 8 1 0.0 0.0
1* 5 0.1 0.1
100 1 0.0 0.0
105 1 0.0 0.0
106 1 0.0 0.0
108 1 0.0 0.0
110 2 0.0 0.0
111 2 0.0 0.0
112 1 0.0 0.0
114 1 0.0 0.0
115 4 0.1 0.1
117 1 0.0 0.0
118 3 0.1 0.1
119 2 0.0 0.0
12 1 0.0 0.0
120 13 0.2 0.2
121 1 0.0 0.0
122 3 0.1 0.1
124 4 0.1 0.1
125 10 0.2 0.2
126 1 0.0 0.0
127 3 0.1 0.1
130 21 0.4 0.4
132 6 0.1 0.1
133 3 0.1 0.1
134 1 0.0 0.0
135 21 0.4 0.4
136 2 0.0 0.0
137 9 0.2 0.2
138 8 0.1 0.2
139 8 0.1 0.2
14* 2 0.0 0.0
140 38 0.7 0.7
141 4 0.1 0.1
142 6 0.1 0.1
143 6 0.1 0.1
144 1 0.0 0.0
145 29 0.5 0.6
146 8 0.1 0.2
147 8 0.1 0.2
148 14 0.2 0.3
149 3 0.1 0.1
150 63 1.1 1.2
151 5 0.1 0.1
152 13 0.2 0.2
153 13 0.2 0.2
154 14 0.2 0.3
155 59 1.0 1.1
156 19 0.3 0.4
157 11 0.2 0.2
158 20 0.3 0.4
159 11 0.2 0.2
16 1 0.0 0.0
160 94 1.6 1.8
161 10 0.2 0.2
162 28 0.5 0.5
163 20 0.3 0.4
164 14 0.2 0.3
165 86 1.5 1.6
166 11 0.2 0.2
167 32 0.5 0.6
168 44 0.8 0.8
169 19 0.3 0.4
17 1 0.0 0.0
170 108 1.8 2.1
171 13 0.2 0.2
172 36 0.6 0.7
173 28 0.5 0.5
174 28 0.5 0.5
175 127 2.2 2.4
176 32 0.5 0.6
177 29 0.5 0.6
178 52 0.9 1.0
179 15 0.3 0.3
18 2 0.0 0.0
18* 1 0.0 0.0
180 146 2.5 2.8
181 15 0.3 0.3
182 43 0.7 0.8
183 35 0.6 0.7
184 37 0.6 0.7
185 136 2.3 2.6
186 27 0.5 0.5
187 34 0.6 0.6
188 49 0.8 0.9
189 41 0.7 0.8
190 150 2.6 2.9
191 12 0.2 0.2
192 44 0.8 0.8
193 29 0.5 0.6
194 17 0.3 0.3
195 121 2.1 2.3
196 32 0.5 0.6
197 41 0.7 0.8
198 56 1.0 1.1
199 26 0.4 0.5
2 18 0.3 0.3
2 * 1 0.0 0.0
2 1 1 0.0 0.0
2 2 1 0.0 0.0
2 6 1 0.0 0.0
2* 9 0.2 0.2
200 163 2.8 3.1
201 10 0.2 0.2
202 31 0.5 0.6
203 21 0.4 0.4
204 35 0.6 0.7
205 122 2.1 2.3
206 18 0.3 0.3
207 32 0.5 0.6
208 31 0.5 0.6
209 23 0.4 0.4
21 1 0.0 0.0
210 163 2.8 3.1
211 15 0.3 0.3
212 49 0.8 0.9
213 15 0.3 0.3
214 39 0.7 0.7
215 135 2.3 2.6
216 14 0.2 0.3
217 17 0.3 0.3
218 43 0.7 0.8
219 13 0.2 0.2
220 146 2.5 2.8
221 15 0.3 0.3
222 27 0.5 0.5
223 27 0.5 0.5
224 18 0.3 0.3
225 83 1.4 1.6
226 20 0.3 0.4
227 17 0.3 0.3
228 19 0.3 0.4
229 12 0.2 0.2
23 1 0.0 0.0
230 127 2.2 2.4
231 5 0.1 0.1
232 22 0.4 0.4
233 21 0.4 0.4
234 25 0.4 0.5
235 83 1.4 1.6
236 11 0.2 0.2
237 17 0.3 0.3
238 22 0.4 0.4
239 4 0.1 0.1
240 102 1.7 1.9
241 10 0.2 0.2
242 25 0.4 0.5
243 12 0.2 0.2
244 10 0.2 0.2
245 63 1.1 1.2
246 14 0.2 0.3
247 17 0.3 0.3
248 12 0.2 0.2
249 14 0.2 0.3
250 92 1.6 1.8
251 6 0.1 0.1
252 11 0.2 0.2
253 6 0.1 0.1
254 15 0.3 0.3
255 30 0.5 0.6
256 6 0.1 0.1
257 6 0.1 0.1
258 5 0.1 0.1
259 8 0.1 0.2
260 66 1.1 1.3
261 4 0.1 0.1
262 15 0.3 0.3
263 6 0.1 0.1
264 7 0.1 0.1
265 40 0.7 0.8
266 9 0.2 0.2
267 7 0.1 0.1
268 6 0.1 0.1
269 3 0.1 0.1
270 50 0.9 1.0
271 5 0.1 0.1
272 6 0.1 0.1
273 4 0.1 0.1
274 4 0.1 0.1
275 19 0.3 0.4
276 4 0.1 0.1
277 7 0.1 0.1
278 5 0.1 0.1
279 4 0.1 0.1
280 37 0.6 0.7
281 3 0.1 0.1
282 2 0.0 0.0
284 2 0.0 0.0
285 16 0.3 0.3
286 2 0.0 0.0
287 3 0.1 0.1
288 3 0.1 0.1
289 8 0.1 0.2
29 1 0.0 0.0
290 14 0.2 0.3
291 1 0.0 0.0
292 4 0.1 0.1
294 3 0.1 0.1
295 13 0.2 0.2
296 2 0.0 0.0
297 6 0.1 0.1
298 9 0.2 0.2
3 1 0.0 0.0
300 24 0.4 0.5
302 2 0.0 0.0
303 1 0.0 0.0
304 1 0.0 0.0
305 4 0.1 0.1
306 1 0.0 0.0
307 3 0.1 0.1
308 1 0.0 0.0
309 3 0.1 0.1
310 13 0.2 0.2
311 2 0.0 0.0
312 2 0.0 0.0
314 1 0.0 0.0
315 6 0.1 0.1
316 1 0.0 0.0
317 3 0.1 0.1
318 1 0.0 0.0
319 1 0.0 0.0
320 8 0.1 0.2
321 3 0.1 0.1
323 1 0.0 0.0
324 2 0.0 0.0
325 6 0.1 0.1
326 2 0.0 0.0
330 11 0.2 0.2
333 1 0.0 0.0
334 1 0.0 0.0
335 4 0.1 0.1
340 4 0.1 0.1
344 1 0.0 0.0
345 2 0.0 0.0
350 5 0.1 0.1
354 1 0.0 0.0
355 2 0.0 0.0
358 1 0.0 0.0
360 1 0.0 0.0
361 1 0.0 0.0
362 1 0.0 0.0
365 2 0.0 0.0
370 1 0.0 0.0
375 1 0.0 0.0
376 1 0.0 0.0
385 1 0.0 0.0
389 1 0.0 0.0
39 1 0.0 0.0
395 1 0.0 0.0
397 1 0.0 0.0
400 3 0.1 0.1
410 1 0.0 0.0
415 1 0.0 0.0
416 1 0.0 0.0
424 1 0.0 0.0
430 1 0.0 0.0
440 1 0.0 0.0
45 1 0.0 0.0
50 1 0.0 0.0
53 1 0.0 0.0
60 1 0.0 0.0
65 4 0.1 0.1
68 3 0.1 0.1
7 1 0.0 0.0
71 1 0.0 0.0
72 2 0.0 0.0
74 2 0.0 0.0
75 1 0.0 0.0
76 1 0.0 0.0
78 1 0.0 0.0
80 5 0.1 0.1
81 1 0.0 0.0
82 1 0.0 0.0
84 1 0.0 0.0
89 1 0.0 0.0
90 3 0.1 0.1
91 1 0.0 0.0
92 1 0.0 0.0
94 1 0.0 0.0
97 2 0.0 0.0
98 1 0.0 0.0
NA 596 10.2 NA
Total 5839 100.0 100.0 
  f2kgs <- d[,"f2kgs"]
  new.d <- data.frame(new.d, f2kgs)
  new.d <- apply_labels(new.d, f2kgs = "weight in lbs")
  temp.d <- data.frame (new.d, f2kgs)  
  result<-questionr::freq(temp.d$f2kgs,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "How much do you current weight? (kgs)")
How much do you current weight? (kgs)
n % val%
* 1 0.0 1.2
0 16 0.3 19.8
1 3 0.1 3.7
1 7 1 0.0 1.2
10 1 0.0 1.2
110 1 0.0 1.2
111 3 0.1 3.7
112 1 0.0 1.2
113 1 0.0 1.2
114 1 0.0 1.2
137 1 0.0 1.2
155 1 0.0 1.2
175 1 0.0 1.2
198 1 0.0 1.2
2 4 0.1 4.9
20 1 0.0 1.2
22 1 0.0 1.2
23 1 0.0 1.2
27 1 0.0 1.2
3 2 0.0 2.5
37 1 0.0 1.2
45 1 0.0 1.2
46 1 0.0 1.2
5 1 0.0 1.2
50 3 0.1 3.7
55 3 0.1 3.7
56 1 0.0 1.2
58 1 0.0 1.2
6 1 0.0 1.2
60 1 0.0 1.2
61 1 0.0 1.2
64 1 0.0 1.2
65 1 0.0 1.2
7 1 0.0 1.2
70 1 0.0 1.2
74 1 0.0 1.2
75 1 0.0 1.2
76 1 0.0 1.2
78 1 0.0 1.2
8 1 0.0 1.2
80 1 0.0 1.2
81 1 0.0 1.2
82 1 0.0 1.2
85 1 0.0 1.2
86 1 0.0 1.2
88 2 0.0 2.5
89 1 0.0 1.2
9 3 0.1 3.7
90 1 0.0 1.2
91 1 0.0 1.2
92 1 0.0 1.2
NA 5758 98.6 NA
Total 5839 100.0 100.0

F3: Exercise frequency

  • F3. How many days per week do you typically get moderate or strenuous exercise (such as heavy lifting, shop work, construction or farm work, home repair, gardening, bowling, golf, jogging, basketball, riding a bike, etc.)?
    • 4=5-7 times per week
    • 3=3-4 times per week
    • 2=1-2 times per week
    • 1=Less than once per week/do not exercise
  f3 <- as.factor(d[,"f3"])
# Make "*" to NA
f3[which(f3=="*")]<-"NA"
  levels(f3) <- list(Per_week_5_7="4",
                     Per_week_3_4="3",
                     Per_week_1_2="2",
                     Per_week_less_1="1")
  f3 <- ordered(f3, c("Per_week_5_7","Per_week_3_4","Per_week_1_2","Per_week_less_1"))
  
  new.d <- data.frame(new.d, f3)
  new.d <- apply_labels(new.d, f3 = "exercise")
  temp.d <- data.frame (new.d, f3)  
  
  result<-questionr::freq(temp.d$f3,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "F3. How many days per week do you typically get moderate or strenuous exercise")
F3. How many days per week do you typically get moderate or strenuous exercise
n % val% %cum val%cum
Per_week_5_7 959 16.4 17.6 16.4 17.6
Per_week_3_4 1593 27.3 29.3 43.7 46.9
Per_week_1_2 1536 26.3 28.2 70.0 75.2
Per_week_less_1 1351 23.1 24.8 93.1 100.0
NA 400 6.9 NA 100.0 NA
Total 5839 100.0 100.0 100.0 100.0

F4: Minutes of exercise

  • F4. On those days that you do moderate or strenuous exercise, how many minutes did you typically exercise at this level?
    • 2=Less than 30 minutes
    • 3=30 minutes – 1 hour
    • 4=More than 1 hour
    • 1=Do not exercise
  f4 <- as.factor(d[,"f4"])
# Make "*" to NA
f4[which(f4=="*")]<-"NA"
  levels(f4) <- list(Less_than_30_min="2",
                     Between_30_min_1_hour="3",
                     More_than_1_hour="4",
                     Do_not_exercise="1")
  f4 <- ordered(f4, c("Less_than_30_min","Between_30_min_1_hour","More_than_1_hour","Do_not_exercise"))
  
  new.d <- data.frame(new.d, f4)
  new.d <- apply_labels(new.d, f4 = "how many minutes exercise")
  temp.d <- data.frame (new.d, f4)  
  
  result<-questionr::freq(temp.d$f4,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "F4")
F4
n % val% %cum val%cum
Less_than_30_min 999 17.1 18.4 17.1 18.4
Between_30_min_1_hour 2258 38.7 41.6 55.8 60.1
More_than_1_hour 1176 20.1 21.7 75.9 81.8
Do_not_exercise 989 16.9 18.2 92.9 100.0
NA 417 7.1 NA 100.0 NA
Total 5839 100.0 100.0 100.0 100.0

F5: Drink alcohol frequency

  • F5. In the past month, about how often do you have at least one drink of any alcoholic beverage such as beer, wine, a malt beverage, or liquor? One drink is equivalent to a 12 oz beer, a 5 oz glass of wine, or a drink with one shot of liquor.
    • 6=Everyday
    • 5=5-6 times per week
    • 4=3-4 times per week
    • 3=1-2 times per week
    • 2=Fewer than once per week
    • 1=Did not drink
  f5 <- as.factor(d[,"f5"])
# Make "*" to NA
f5[which(f5=="*")]<-"NA"
  levels(f5) <- list(Everyday="6",
                     Per_week_5_6_times="5",
                     Per_week_3_4_times="4",
                     Per_week_1_2_times="3",
                     Per_week_fewer_once="2",
                     Not_drink="1")
  f5 <- ordered(f5, c("Everyday","Per_week_5_6_times","Per_week_3_4_times","Per_week_1_2_times","Per_week_fewer_once","Not_drink"))
  
  new.d <- data.frame(new.d, f5)
  new.d <- apply_labels(new.d, f5 = "how often drink")
  temp.d <- data.frame (new.d, f5)  
  
  result<-questionr::freq(temp.d$f5,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f5")
f5
n % val% %cum val%cum
Everyday 275 4.7 4.8 4.7 4.8
Per_week_5_6_times 244 4.2 4.3 8.9 9.1
Per_week_3_4_times 628 10.8 11.0 19.6 20.1
Per_week_1_2_times 842 14.4 14.8 34.1 34.9
Per_week_fewer_once 1163 19.9 20.4 54.0 55.3
Not_drink 2547 43.6 44.7 97.6 100.0
NA 140 2.4 NA 100.0 NA
Total 5839 100.0 100.0 100.0 100.0

F6: How many drinks

  • F6. When you drank during the past month, how many drinks do you have on a typical occasion?
    • 3=3 or more drinks
    • 2=1-2 drinks
    • 1=Did not drink
  f6 <- as.factor(d[,"f6"])
# Make "*" to NA
f6[which(f6=="*")]<-"NA"
  levels(f6) <- list(Three_or_more="3",
                     One_to_two_drinks="2",
                     Not_drink="1")
  f6 <- ordered(f6, c("Three_or_more","One_to_two_drinks","Not_drink"))
  
  new.d <- data.frame(new.d, f6)
  new.d <- apply_labels(new.d, f6 = "how many drinks")
  temp.d <- data.frame (new.d, f6)  
  
  result<-questionr::freq(temp.d$f6,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f6")
f6
n % val% %cum val%cum
Three_or_more 540 9.2 10.4 9.2 10.4
One_to_two_drinks 2473 42.4 47.8 51.6 58.3
Not_drink 2159 37.0 41.7 88.6 100.0
NA 667 11.4 NA 100.0 NA
Total 5839 100.0 100.0 100.0 100.0

F7: Smoking history

  • F7. Have you ever smoked at least 100 cigarettes in your lifetime?
    • 1=No
    • 2=Yes
  • F7Age. If yes, At what age did you start smoking on a regular basis (at least one cigarette/day)?
    • 555 = “Less than 10”
    • 777 = “75+”
  • F7a. How many cigarettes do you (or did you) usually smoke per day?
    • 1=1-5
    • 2=6-10
    • 3=11-20
    • 4=21-30
    • 5=31+
  • F7b. Have you quit smoking?
    • 1=No
    • 2=Yes
  • F7BAge. If yes, At what age did you quit?
    • 555 = “Less than 10”
    • 777 = “75+”
  f7 <- as.factor(d[,"f7"])
# Make "*" to NA
f7[which(f7=="*")]<-"NA"
  levels(f7) <- list(Yes="2",
                     No="1")
  f7 <- ordered(f7, c("No","Yes"))
  
  new.d <- data.frame(new.d, f7)
  new.d <- apply_labels(new.d, f7 = "smoke")
  temp.d <- data.frame (new.d, f7)  
  
  result<-questionr::freq(temp.d$f7,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "F7. Have you ever smoked at least 100 cigarettes in your lifetime?")
F7. Have you ever smoked at least 100 cigarettes in your lifetime?
n % val% %cum val%cum
No 3008 51.5 54.1 51.5 54.1
Yes 2556 43.8 45.9 95.3 100.0
NA 275 4.7 NA 100.0 NA
Total 5839 100.0 100.0 100.0 100.0 
  f7age <- d[,"f7age"]
  f7age[which(f7age=="555")]<-"Less_than_10"
  f7age[which(f7age=="777")]<-"More_than_75"

  new.d <- data.frame(new.d, f7age)
  new.d <- apply_labels(new.d, f7age = "age start to smoke")
  temp.d <- data.frame (new.d, f7age)  
  
  result<-questionr::freq(temp.d$f7age,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "F7Age. If yes, At what age did you start smoking on a regular basis (at least one cigarette/day)?")
F7Age. If yes, At what age did you start smoking on a regular basis (at least one cigarette/day)?
n % val%
0 10 0.2 0.5
1 4 0.1 0.2
10 14 0.2 0.7
11 11 0.2 0.5
12 41 0.7 2.0
13 60 1.0 2.9
14 76 1.3 3.7
15 183 3.1 8.8
16 229 3.9 11.0
17 206 3.5 9.9
18 321 5.5 15.5
19 168 2.9 8.1
2 2 0.0 0.1
20 201 3.4 9.7
21 116 2.0 5.6
22 66 1.1 3.2
23 47 0.8 2.3
24 29 0.5 1.4
25 67 1.1 3.2
26 14 0.2 0.7
27 17 0.3 0.8
28 22 0.4 1.1
29 6 0.1 0.3
3 3 0.1 0.1
30 45 0.8 2.2
31 2 0.0 0.1
32 8 0.1 0.4
33 7 0.1 0.3
34 6 0.1 0.3
35 21 0.4 1.0
36 1 0.0 0.0
38 3 0.1 0.1
4 1 0.0 0.0
40 14 0.2 0.7
41 1 0.0 0.0
42 1 0.0 0.0
44 1 0.0 0.0
45 5 0.1 0.2
47 3 0.1 0.1
48 3 0.1 0.1
49 4 0.1 0.2
5 1 0.0 0.0
50 5 0.1 0.2
54 2 0.0 0.1
55 2 0.0 0.1
56 1 0.0 0.0
59 1 0.0 0.0
6 1 0.0 0.0
60 2 0.0 0.1
62 1 0.0 0.0
65 1 0.0 0.0
69 2 0.0 0.1
7 4 0.1 0.2
70 1 0.0 0.0
8 4 0.1 0.2
9 7 0.1 0.3
Less_than_10 2 0.0 0.1
NA 3763 64.4 NA
Total 5839 100.0 100.0 
  f7a <- as.factor(d[,"f7a"])
  # Make "*" to NA
f7a[which(f7a=="*")]<-"NA"
  levels(f7a) <- list(One_to_five="1",
                     Six_to_ten="2",
                     Eleven_to_twenty="3",
                     Twentyone_to_Thirty="4",
                     Older_31="5")
  f7a <- ordered(f7a, c("One_to_five","Six_to_ten","Eleven_to_twenty","Twentyone_to_Thirty","Older_31"))

  new.d <- data.frame(new.d, f7a)
  new.d <- apply_labels(new.d, f7a = "How many cigarettes per day")
  temp.d <- data.frame (new.d, f7a)  
  
  result<-questionr::freq(temp.d$f7a,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "F7a. How many cigarettes do you (or did you) usually smoke per day?")
F7a. How many cigarettes do you (or did you) usually smoke per day?
n % val% %cum val%cum
One_to_five 917 15.7 35.4 15.7 35.4
Six_to_ten 774 13.3 29.9 29.0 65.2
Eleven_to_twenty 664 11.4 25.6 40.3 90.9
Twentyone_to_Thirty 168 2.9 6.5 43.2 97.3
Older_31 69 1.2 2.7 44.4 100.0
NA 3247 55.6 NA 100.0 NA
Total 5839 100.0 100.0 100.0 100.0 
  f7b <- as.factor(d[,"f7b"])
    # Make "*" to NA
f7b[which(f7b=="*")]<-"NA"
  levels(f7b) <- list(No="1",
                     Yes="2")

  new.d <- data.frame(new.d, f7b)
  new.d <- apply_labels(new.d, f7b = "quit smoking")
  temp.d <- data.frame (new.d, f7b)  
  
  result<-questionr::freq(temp.d$f7b,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "F7b. Have you quit smoking?")
F7b. Have you quit smoking?
n % val%
No 572 9.8 21.7
Yes 2068 35.4 78.3
NA 3199 54.8 NA
Total 5839 100.0 100.0 
  f7bage <- d[,"f7bage"]
  f7bage[which(f7bage=="555")]<-"Less_than_10"
  f7bage[which(f7bage=="777")]<-"More_than_75"

  new.d <- data.frame(new.d, f7bage)
  new.d <- apply_labels(new.d, f7bage = "age quit smoking")
  temp.d <- data.frame (new.d, f7bage)  
  
  result<-questionr::freq(temp.d$f7bage,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "F7BAge. If yes, At what age did you quit?")
F7BAge. If yes, At what age did you quit?
n % val%
1 2 0.0 0.1
11 2 0.0 0.1
12 1 0.0 0.1
14 1 0.0 0.1
15 2 0.0 0.1
16 6 0.1 0.3
17 8 0.1 0.4
18 8 0.1 0.4
19 13 0.2 0.7
20 36 0.6 1.8
21 32 0.5 1.6
22 26 0.4 1.3
23 38 0.7 1.9
24 27 0.5 1.4
25 61 1.0 3.1
26 21 0.4 1.1
27 27 0.5 1.4
28 50 0.9 2.5
29 25 0.4 1.3
30 106 1.8 5.3
31 33 0.6 1.7
32 30 0.5 1.5
33 27 0.5 1.4
34 30 0.5 1.5
35 110 1.9 5.5
36 24 0.4 1.2
37 18 0.3 0.9
38 31 0.5 1.6
39 23 0.4 1.2
4 2 0.0 0.1
40 133 2.3 6.7
41 26 0.4 1.3
42 37 0.6 1.9
43 25 0.4 1.3
44 19 0.3 1.0
45 78 1.3 3.9
46 20 0.3 1.0
47 26 0.4 1.3
48 32 0.5 1.6
49 27 0.5 1.4
50 132 2.3 6.6
51 25 0.4 1.3
52 37 0.6 1.9
53 22 0.4 1.1
54 23 0.4 1.2
55 63 1.1 3.2
56 33 0.6 1.7
57 23 0.4 1.2
58 38 0.7 1.9
59 32 0.5 1.6
6 1 0.0 0.1
60 61 1.0 3.1
61 20 0.3 1.0
62 33 0.6 1.7
63 22 0.4 1.1
64 18 0.3 0.9
65 45 0.8 2.3
66 19 0.3 1.0
67 17 0.3 0.9
68 18 0.3 0.9
69 16 0.3 0.8
7 1 0.0 0.1
70 19 0.3 1.0
71 8 0.1 0.4
72 4 0.1 0.2
73 5 0.1 0.3
74 4 0.1 0.2
75 3 0.1 0.2
76 2 0.0 0.1
78 3 0.1 0.2
79 1 0.0 0.1
8 2 0.0 0.1
80 1 0.0 0.1
86 1 0.0 0.1
9 1 0.0 0.1
NA 3843 65.8 NA
Total 5839 100.0 100.0

G1: Marital status

  • G1. What is your current marital status?
    • 1=Married, or living with a partner
    • 2=Separated
    • 3=Divorced
    • 4=Widowed
    • 5=Never Married
  g1 <- as.factor(d[,"g1"])
  # Make "*" to NA
g1[which(g1=="*")]<-"NA"
  levels(g1) <- list(Married_partner="1",
                     Separated="2",
                     Divorced="3",
                     Widowed="4",
                     Never_Married="5")
  g1 <- ordered(g1, c("Married_partner","Separated","Divorced","Widowed","Never_Married"))
  
  new.d <- data.frame(new.d, g1)
  new.d <- apply_labels(new.d, g1 = "marital status")
  temp.d <- data.frame (new.d, g1)  
  
  result<-questionr::freq(temp.d$g1,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "g1")
g1
n % val% %cum val%cum
Married_partner 3682 63.1 64.5 63.1 64.5
Separated 204 3.5 3.6 66.6 68.0
Divorced 928 15.9 16.2 82.4 84.3
Widowed 353 6.0 6.2 88.5 90.5
Never_Married 545 9.3 9.5 97.8 100.0
NA 127 2.2 NA 100.0 NA
Total 5839 100.0 100.0 100.0 100.0

G2: With whom do you live

  • G2. With whom do you live? Mark all that apply.
    • G2_1: 1=Live alone
    • G2_2: 1=A spouse or partner
    • G2_3: 1=Other family
    • G2_4: 1=Other people (non-family)
    • G2_5: 1=Pets
  g2_1 <- as.factor(d[,"g2_1"])
  levels(g2_1) <- list(Live_alone="1")

  new.d <- data.frame(new.d, g2_1)
  new.d <- apply_labels(new.d, g2_1 = "Live alone")
  temp.d <- data.frame (new.d, g2_1)  
  
  result<-questionr::freq(temp.d$g2_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g2_1: Live alone")
g2_1: Live alone
n % val%
Live_alone 1273 21.8 100
NA 4566 78.2 NA
Total 5839 100.0 100 
  g2_2 <- as.factor(d[,"g2_2"])
  levels(g2_2) <- list(spouse_partner="1")

  new.d <- data.frame(new.d, g2_2)
  new.d <- apply_labels(new.d, g2_2 = "A spouse or partner")
  temp.d <- data.frame (new.d, g2_2)  
  
  result<-questionr::freq(temp.d$g2_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g2_2: A spouse or partner")
g2_2: A spouse or partner
n % val%
spouse_partner 3798 65 100
NA 2041 35 NA
Total 5839 100 100 
  g2_3 <- as.factor(d[,"g2_3"])
  levels(g2_3) <- list(Other_family="1")

  new.d <- data.frame(new.d, g2_3)
  new.d <- apply_labels(new.d, g2_3 = "Other family")
  temp.d <- data.frame (new.d, g2_3)  
  
  result<-questionr::freq(temp.d$g2_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g2_3: Other family")
g2_3: Other family
n % val%
Other_family 819 14 100
NA 5020 86 NA
Total 5839 100 100 
  g2_4 <- as.factor(d[,"g2_4"])
  levels(g2_4) <- list(Other_non_family="1")

  new.d <- data.frame(new.d, g2_4)
  new.d <- apply_labels(new.d, g2_4 = "Other people (non-family)")
  temp.d <- data.frame (new.d, g2_4)  
  
  result<-questionr::freq(temp.d$g2_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g2_4: Other people (non-family)")
g2_4: Other people (non-family)
n % val%
Other_non_family 134 2.3 100
NA 5705 97.7 NA
Total 5839 100.0 100 
  g2_5 <- as.factor(d[,"g2_5"])
  levels(g2_5) <- list(Pets="1")

  new.d <- data.frame(new.d, g2_5)
  new.d <- apply_labels(new.d, g2_5 = "Pets")
  temp.d <- data.frame (new.d, g2_5)  
  
  result<-questionr::freq(temp.d$g2_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g2_5: Pets")
g2_5: Pets
n % val%
Pets 345 5.9 100
NA 5494 94.1 NA
Total 5839 100.0 100

G3: Identify yourself

  • G3. How do you identify yourself?
    • 1=Straight/heterosexual
    • 2=Bisexual
    • 3=Gay/homosexual/same gender loving
    • 4=Other
    • 99=Prefer not to answer
  g3 <- as.factor(d[,"g3"])
  # Make "*" to NA
g3[which(g3=="*")]<-"NA"
  levels(g3) <- list(heterosexual="1",
                      Bisexual="2",
                       homosexual="3",
                       Other="4",
                       Prefer_not_to_answer="99")
  g3 <- ordered(g3, c("heterosexual","Bisexual","homosexual","Other","Prefer_not_to_answer"))

  new.d <- data.frame(new.d, g3)
  new.d <- apply_labels(new.d, g3 = "identify yourself")
  temp.d <- data.frame (new.d, g3)  
  
  result<-questionr::freq(temp.d$g3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g3")
g3
n % val%
heterosexual 5366 91.9 95.1
Bisexual 38 0.7 0.7
homosexual 102 1.7 1.8
Other 30 0.5 0.5
Prefer_not_to_answer 108 1.8 1.9
NA 195 3.3 NA
Total 5839 100.0 100.0

G3 Other: Identify yourself

g3other <- d[,"g3other"]
  new.d <- data.frame(new.d, g3other)
  new.d <- apply_labels(new.d, g3other = "g3other")
  temp.d <- data.frame (new.d, g3other)
result<-questionr::freq(temp.d$g3other, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "G3 Other")
G3 Other
n % val%
—- 1 0.0 1.4
100% man 1 0.0 1.4
a man 1 0.0 1.4
A man 3 0.1 4.2
A person straight. 1 0.0 1.4
All man 1 0.0 1.4
All man by the grace of God 1 0.0 1.4
Allosexual 1 0.0 1.4
Black-A 1 0.0 1.4
But not sexually active 1 0.0 1.4
Christian. 1 0.0 1.4
Do not want to be confused like women nothing else 1 0.0 1.4
Get hard, I wish it would. 1 0.0 1.4
Have a man! He took something from me that I can’t get back. 1 0.0 1.4
Human 2 0.0 2.8
Human! 1 0.0 1.4
I am man married to woman 1 0.0 1.4
I am straight 1 0.0 1.4
I am straight. 2 0.0 2.8
I consider myself a white girl 1 0.0 1.4
I enjoy women only. 1 0.0 1.4
Just straight 1 0.0 1.4
Love women only 1 0.0 1.4
Male 3 0.1 4.2
Male/Married 1 0.0 1.4
Man 3 0.1 4.2
Man (male) 1 0.0 1.4
Man straight 1 0.0 1.4
Man’s man 1 0.0 1.4
Man/Human being 1 0.0 1.4
Married 1 0.0 1.4
No 1 0.0 1.4
No sex 1 0.0 1.4
None heterosexual 1 0.0 1.4
Normal human being 1 0.0 1.4
Normal man 1 0.0 1.4
Not gay thank God. 1 0.0 1.4
Self 1 0.0 1.4
Start 1 0.0 1.4
Straight 9 0.2 12.7
Straight earl. 1 0.0 1.4
Straight I like women 1 0.0 1.4
Straight male 2 0.0 2.8
Straight male. 1 0.0 1.4
Straight man 1 0.0 1.4
Straight man all 1 0.0 1.4
Straight person 1 0.0 1.4
Straight. 3 0.1 4.2
Strong black man 1 0.0 1.4
What God made me a man 1 0.0 1.4
Whole man. 1 0.0 1.4
With a woman 1 0.0 1.4
NA 5768 98.8 NA
Total 5839 100.0 100.0

G4: Education

  • G4. What is the HIGHEST level of education you, your father, and your mother have completed?
    • 1=Grade school or less
    • 2=Some high school
    • 3=High school graduate or GED
    • 4=Vocational school
    • 5=Some college
    • 6=Associate’s degree
    • 7=College graduate (Bachelor’s degree)
    • 8=Some graduate education
    • 9=Graduate degree
    • 88=Don’t know
  g4a <- as.factor(d[,"g4a"])
  # Make "*" to NA
g4a[which(g4a=="*")]<-"NA"
  levels(g4a) <- list(Grade_school_or_less="1",
                      Some_high_school="2",
                       High_school_graduate_GED="3",
                       Vocational_school="4",
                      Some_college="5",
                      Associate_degree="6",
                      College_graduate="7",
                      Some_graduate_education="8",
                      Graduate_degree="9")
  g4a <- ordered(g4a, c("Grade_school_or_less","Some_high_school","High_school_graduate_GED","Vocational_school","Some_college","Associate_degree","College_graduate","Some_graduate_education","Graduate_degree"))

  new.d <- data.frame(new.d, g4a)
  new.d <- apply_labels(new.d, g4a = "education")
  temp.d <- data.frame (new.d, g4a)  
  
  result<-questionr::freq(temp.d$g4a,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g4a: You")
g4a: You
n % val%
Grade_school_or_less 139 2.4 2.6
Some_high_school 372 6.4 7.0
High_school_graduate_GED 1170 20.0 22.0
Vocational_school 188 3.2 3.5
Some_college 1221 20.9 23.0
Associate_degree 482 8.3 9.1
College_graduate 777 13.3 14.6
Some_graduate_education 194 3.3 3.6
Graduate_degree 774 13.3 14.6
NA 522 8.9 NA
Total 5839 100.0 100.0 
  g4b <- as.factor(d[,"g4b"])
    # Make "*" to NA
g4b[which(g4b=="*")]<-"NA"
  levels(g4b) <- list(Grade_school_or_less="1",
                      Some_high_school="2",
                       High_school_graduate_GED="3",
                       Vocational_school="4",
                      Some_college="5",
                      Associate_degree="6",
                      College_graduate="7",
                      Some_graduate_education="8",
                      Graduate_degree="9",
                      Dont_know="88")
  g4b <- ordered(g4b, c("Grade_school_or_less","Some_high_school","High_school_graduate_GED","Vocational_school","Some_college","Associate_degree","College_graduate","Some_graduate_education","Graduate_degree","Dont_know"))

  new.d <- data.frame(new.d, g4b)
  new.d <- apply_labels(new.d, g4b = "education-father")
  temp.d <- data.frame (new.d, g4b)  
  
  result<-questionr::freq(temp.d$g4b,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g4b: Your father")
g4b: Your father
n % val%
Grade_school_or_less 1324 22.7 25.3
Some_high_school 740 12.7 14.1
High_school_graduate_GED 1191 20.4 22.7
Vocational_school 162 2.8 3.1
Some_college 215 3.7 4.1
Associate_degree 89 1.5 1.7
College_graduate 190 3.3 3.6
Some_graduate_education 31 0.5 0.6
Graduate_degree 149 2.6 2.8
Dont_know 1145 19.6 21.9
NA 603 10.3 NA
Total 5839 100.0 100.0 
  g4c <- as.factor(d[,"g4c"])
    # Make "*" to NA
g4c[which(g4c=="*")]<-"NA"
  levels(g4c) <- list(Grade_school_or_less="1",
                      Some_high_school="2",
                       High_school_graduate_GED="3",
                       Vocational_school="4",
                      Some_college="5",
                      Associate_degree="6",
                      College_graduate="7",
                      Some_graduate_education="8",
                      Graduate_degree="9",
                      Dont_know="88")
  g4c <- ordered(g4c, c("Grade_school_or_less","Some_high_school","High_school_graduate_GED","Vocational_school","Some_college","Associate_degree","College_graduate","Some_graduate_education","Graduate_degree","Dont_know"))

  new.d <- data.frame(new.d, g4c)
  new.d <- apply_labels(new.d, g4c = "education-mother")
  temp.d <- data.frame (new.d, g4c)  
  
  result<-questionr::freq(temp.d$g4c,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g4c: Your mother")
g4c: Your mother
n % val%
Grade_school_or_less 965 16.5 18.4
Some_high_school 824 14.1 15.7
High_school_graduate_GED 1522 26.1 29.0
Vocational_school 210 3.6 4.0
Some_college 306 5.2 5.8
Associate_degree 172 2.9 3.3
College_graduate 257 4.4 4.9
Some_graduate_education 38 0.7 0.7
Graduate_degree 185 3.2 3.5
Dont_know 761 13.0 14.5
NA 599 10.3 NA
Total 5839 100.0 100.0

G5: Job

  • G5. Which one of the following best describes what you currently do?
    • 1=Currently working full-time
    • 2=Currently working part-time
    • 3=Looking for work, unemployed
    • 4=Retired
    • 5=On disability permanently
    • 6=On disability for a period of time (on sick leave or paternity leave or disability leave for other reasons)
    • 7=Volunteer work/work without pay
    • 8=Other
  g5 <- as.factor(d[,"g5"])
  # Make "*" to NA
g5[which(g5=="*")]<-"NA"
  levels(g5) <- list(full_time="1",
                     part_time="2",
                     unemployed="3",
                     Retired="4",
                     disability_permanently="5",
                     disability_for_a_time="6",
                     Volunteer_work="7",
                     Other="8")
  g5 <- ordered(g5, c("full_time","part_time","unemployed","Retired","disability_permanently","disability_for_a_time", "Volunteer_work","Other"))

  new.d <- data.frame(new.d, g5)
  new.d <- apply_labels(new.d, g5 = "job")
  temp.d <- data.frame (new.d, g5)  
  
  result<-questionr::freq(temp.d$g5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g5")
g5
n % val%
full_time 1514 25.9 27.4
part_time 313 5.4 5.7
unemployed 96 1.6 1.7
Retired 2688 46.0 48.7
disability_permanently 685 11.7 12.4
disability_for_a_time 59 1.0 1.1
Volunteer_work 32 0.5 0.6
Other 130 2.2 2.4
NA 322 5.5 NA
Total 5839 100.0 100.0

G5 Other: job

g5other <- d[,"g5other"]
  new.d <- data.frame(new.d, g5other)
  new.d <- apply_labels(new.d, g5other = "g5other")
  temp.d <- data.frame (new.d, g5other)
result<-questionr::freq(temp.d$g5other, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "G5 Other")
G5 Other
n % val%
100% physically and mentally disabled. 1 0.0 0.4
60% disable veteran 1 0.0 0.4
Also perform volunteer work each week 1 0.0 0.4
And publish a newspaper. 1 0.0 0.4
Artist 1 0.0 0.4
back and forth to doctor but need work (looking for work, unemployed) 1 0.0 0.4
business owner 1 0.0 0.4
Business Owner 1 0.0 0.4
Can’t make but $85 over my monthly allotment. 1 0.0 0.4
Church. 1 0.0 0.4
Coach 1 0.0 0.4
Combat wounded in Vietnam 1 0.0 0.4
Commissioner at voting pole 1 0.0 0.4
Computer consultant. 1 0.0 0.4
Contract work 1 0.0 0.4
Corona virus shut down 1 0.0 0.4
Covid 19 1 0.0 0.4
Covid-19 Laid Off 1 0.0 0.4
Covid-19. Work part-time for SFUSD schools. 1 0.0 0.4
Currently laid off, because of Covid 19. 1 0.0 0.4
Currently on Furlough due to the CoVid-19 Pandemic 1 0.0 0.4
Cut grass and pick up cans. 1 0.0 0.4
DDS, Dentist practicing 2 days a week 1 0.0 0.4
Digital Art work 1 0.0 0.4
Disability retirement 1 0.0 0.4
Disabled American veteran. 1 0.0 0.4
Disabled from birth, intellectually disabled. 1 0.0 0.4
Disabled from my deadly double 18 wheeler truck. The truck driver died. 1 0.0 0.4
disabled self-employed 1 0.0 0.4
Disabled vet/retired from DHS 1 0.0 0.4
Do help my daughter with cleaning offices. 1 0.0 0.4
Due to pandemic, working continuously for 45-plus years. Worked two jobs for 15 years. 1 0.0 0.4
Elected official-city council 1 0.0 0.4
Entrepreneur 1 0.0 0.4
Force to Social Security 1 0.0 0.4
Full-time farmer. 1 0.0 0.4
full-time graduate student and work part-time 1 0.0 0.4
Furloughed COVID 19 Pandemic 1 0.0 0.4
Had surgery 7-30-20, unable to work at this time. 1 0.0 0.4
Health care/hospital shipping and receiving 1 0.0 0.4
Hurt On The Job 1 0.0 0.4
I also work as Uber driver part time 1 0.0 0.4
I am a very old 95 year old man. 1 0.0 0.4
I am living off Gov. income —-. 1 0.0 0.4
I am self employed 1 0.0 0.4
I have had at least 6 heart attacks and currently have 9 stints in my heart and have been diagnosed with heart failure. Waiting on disability. 1 0.0 0.4
I have had at least 6 heart attacks, 9 stints —- have heart —- waiting on disability. 1 0.0 0.4
I still work sometimes. 1 0.0 0.4
I was working fulltime as a retail manager up until covid-19. Last worked March 2020. 1 0.0 0.4
I work in the information/technology sector, due to the importation of foreign workers, I have on worked 4.5 years since 2005, racism has been a large reason for my unemployment 1 0.0 0.4
I work part time and I am self employed 1 0.0 0.4
I’m a lawyer for 44 years. 1 0.0 0.4
I’m on SSDI trying to go back to work. 1 0.0 0.4
Import/retail sale of handmade crafts after retirement. 1 0.0 0.4
In business. 1 0.0 0.4
Insurance Agent: slow due to covid 1 0.0 0.4
Justice of the peace St. John Parish 1 0.0 0.4
Lack of work because of Covid 19 1 0.0 0.4
Lawn and lawn mower repair occasionally 1 0.0 0.4
Looking to work soon 1 0.0 0.4
Lung cancer stage 4-hospice care. 1 0.0 0.4
Manage income property 1 0.0 0.4
Medically retired due to surgery got to have more surgery. 1 0.0 0.4
Mentoring 1st year principals 1 0.0 0.4
Minister 1 0.0 0.4
Minister, author, poet 1 0.0 0.4
Mow lawns, remodel houses. 1 0.0 0.4
Musician 2 0.0 0.8
My family help with my bills and rent 1 0.0 0.4
Need to be on disability permanently 1 0.0 0.4
No work due to covid/district for which I sub, will hopefully return to normal in September. 1 0.0 0.4
Not able to work 1 0.0 0.4
Not able to work I have a bad back, I’m trying to get disability. 1 0.0 0.4
Not working due to Covid 19 pandemic 1 0.0 0.4
Not working, don’t feel good all the time. 1 0.0 0.4
Nothing at the moment 1 0.0 0.4
Now receiving Social Security benefits 1 0.0 0.4
Occasional Uber driver 1 0.0 0.4
Off work due to medical treatments ongoing. 1 0.0 0.4
On disability due to heart procedure. 1 0.0 0.4
On disability, for another year, had kidney transplant 1 0.0 0.4
On disability. 1 0.0 0.4
on social security 1 0.0 0.4
On temporary disability (workers compensation) 1 0.0 0.4
Out on sick leave, after Liphoma removal. 1 0.0 0.4
Own a business 1 0.0 0.4
Own my own business. 1 0.0 0.4
Own PC home 1 0.0 0.4
Own vending business 1 0.0 0.4
Pandemic furlough since 3-2020. 1 0.0 0.4
Part time business owner of ACN. 1 0.0 0.4
Part time peel around 1 0.0 0.4
Part time self employed 2 0.0 0.8
Part time, business owner (pandemic) 1 0.0 0.4
part-time/retired 1 0.0 0.4
Pastor 1 0.0 0.4
Pastor at church 1 0.0 0.4
Pastor. 1 0.0 0.4
PhD program and part time work 1 0.0 0.4
Photographer work from home table top photos 1 0.0 0.4
Plus photographer 1 0.0 0.4
Prefer not to answer. 1 0.0 0.4
Private investor 1 0.0 0.4
recently laid off, now deciding between retirement and job search 1 0.0 0.4
Regular temp 1 0.0 0.4
Retired 14 0.2 5.8
Retired (2005) 1 0.0 0.4
retired (human resources) but off and on part-time and full time self (EdD level education projects and consulting))) 1 0.0 0.4
Retired 23 years from city of Los Angeles 1 0.0 0.4
Retired after 30 years in mental health. Working part time in D.D. 1 0.0 0.4
Retired and disability permanently 1 0.0 0.4
Retired and full-time doctoral student 1 0.0 0.4
retired and on disability permanently 1 0.0 0.4
Retired and on disability permanently 1 0.0 0.4
Retired and part time work 1 0.0 0.4
Retired and Self Employed 1 0.0 0.4
Retired and worked as consultant 1 0.0 0.4
Retired and working 1 0.0 0.4
Retired but assisting partner at her business daily 1 0.0 0.4
Retired but do volunteer work, sit on board. 1 0.0 0.4
Retired but does a lot of work for the church and is a photographer 1 0.0 0.4
Retired but doing some work (acting) 1 0.0 0.4
retired but work a little to stay active 1 0.0 0.4
Retired but work part time, 2 days a week. 1 0.0 0.4
Retired but would love to work. 1 0.0 0.4
retired by still works fulltime 1 0.0 0.4
Retired consultant ESL teacher 4 days 7 hour a day 1 0.0 0.4
Retired fighting for permanent from VA. 1 0.0 0.4
Retired I do in home care PT 1 0.0 0.4
Retired military 2 0.0 0.8
Retired military and public education and now pastor of a CME church. 1 0.0 0.4
Retired military working full time security 1 0.0 0.4
Retired military. 1 0.0 0.4
retired part time work 1 0.0 0.4
Retired part-time work —-. 1 0.0 0.4
Retired professional firefighter-Fire Captain 1 0.0 0.4
Retired sitter for my mother 1 0.0 0.4
Retired with Pension and have small construction business 1 0.0 0.4
Retired-part time insurance sales 1 0.0 0.4
retired, but doing consulting work 1 0.0 0.4
Retired, but looking for work. 1 0.0 0.4
Retired, but serve on multiple boards and do a great deal of community work, including using the arts (e.g., theatre, television, film) to promote health equity and social justice. 1 0.0 0.4
Retired, but work on our farm, some construction 1 0.0 0.4
Retired, plus volunteer work 1 0.0 0.4
Retired, volunteer (W/P) 1 0.0 0.4
Retired, work on occassions 1 0.0 0.4
Retired, working part time 1 0.0 0.4
retired/disabled 1 0.0 0.4
Retiring 6-30-2021, Laco Fire Dept.. 1 0.0 0.4
Sales consultant 1 0.0 0.4
school crossing guard 1 0.0 0.4
Seeking disability. 1 0.0 0.4
Self 2 0.0 0.8
self employed 3 0.1 1.2
Self employed 15 0.3 6.2
Self Employed 1 0.0 0.4
SELF EMPLOYED 1 0.0 0.4
Self employed auto mechanic 1 0.0 0.4
Self employed in property management and non profit work 1 0.0 0.4
Self employed martial arts instructor —- 1 0.0 0.4
Self employed. 1 0.0 0.4
self-employed 1 0.0 0.4
Self-employed (caterer). 1 0.0 0.4
self-employed tax preparer 1 0.0 0.4
Self-employed. 1 0.0 0.4
Semi retired (programmer) 1 0.0 0.4
Semi retired, work 3 days a week, 27 hours a week 1 0.0 0.4
semi-retired 2 0.0 0.8
Several jobs in the side such as: mowing lawns, cleaning buildings. 1 0.0 0.4
Ship captain 1 0.0 0.4
Sick leave. 1 0.0 0.4
Simi Retired- self employed 1 0.0 0.4
Since April of 2020, I have not had my own residence and have been living with family or friends. 1 0.0 0.4
Small business owner 1 0.0 0.4
Social security 1 0.0 0.4
Social Security 2 0.0 0.8
Social Security income SSI. 1 0.0 0.4
Sold business 11/2018. Semi-Retired. 1 0.0 0.4
SS 1 0.0 0.4
SSI 3 0.1 1.2
Started Rail Consultant company 1 0.0 0.4
Starting new home-healthcare and insurance brokerage business 1 0.0 0.4
Stay home now, because of Corona Virus. 1 0.0 0.4
Sub/teacher/Magnet School/Townview 1 0.0 0.4
Substitute teacher/graphic designer website designer 1 0.0 0.4
Take care of a 98 year old man. 1 0.0 0.4
Teach tennis free to kids 1 0.0 0.4
Technical contract worker 1 0.0 0.4
Temp job 1 0.0 0.4
Trying to get disability 1 0.0 0.4
Unemployed 2 0.0 0.8
Unemployment over nine years. 1 0.0 0.4
Varies 1 0.0 0.4
Vet disability Vietnam 1 0.0 0.4
Vocational 1 0.0 0.4
Volunteer at church 1 0.0 0.4
Work full with disability aka left 1 0.0 0.4
Work to supplement Retirement Income 1 0.0 0.4
Workers compensation 1 0.0 0.4
Working full time as a retired annuitant. 1 0.0 0.4
Working occasionally. 1 0.0 0.4
Write 1 0.0 0.4
yard work 1 0.0 0.4
Yard work. 1 0.0 0.4
NA 5597 95.9 NA
Total 5839 100.0 100.0

G6: Health insurance

  • G6. What kind of health insurance or health care coverage do you currently have? Mark all that apply.
    • G6_1: 1=Insurance provided through my current or former employer or union (including Kaiser/HMO/PPO)
    • G6_2: 1=Insurance provided by another family member (e.g., spouse) through their current or former employer or union (including Kaiser/HMO/PPO)
    • G6_3: 1=Insurance purchased directly from an insurance company (by you or another family member)
    • G6_4: 1=Insurance purchased from an exchange (sometimes called Obamacare or the Affordable Care Act)
    • G6_5: 1= Medicaid or other state provided insurance
    • G6_6: 1=Medicare/government insurance
    • G6_7: 1=VA/Military Facility (including those who have ever used or enrolled for VA health care)
    • G6_8: 1=I do not have any medical insurance
  g6_1 <- as.factor(d[,"g6_1"])
  levels(g6_1) <- list(Insurance_employer="1")
  new.d <- data.frame(new.d, g6_1)
  new.d <- apply_labels(new.d, g6_1 = "Insurance_employer")
  temp.d <- data.frame (new.d, g6_1)  
  result<-questionr::freq(temp.d$g6_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "G6_1. Insurance provided through my current or former employer or union (including Kaiser/HMO/PPO)")
G6_1. Insurance provided through my current or former employer or union (including Kaiser/HMO/PPO)
n % val%
Insurance_employer 2084 35.7 100
NA 3755 64.3 NA
Total 5839 100.0 100 
  g6_2 <- as.factor(d[,"g6_2"])
  levels(g6_2) <- list(Insurance_family="1")
  new.d <- data.frame(new.d, g6_2)
  new.d <- apply_labels(new.d, g6_2 = "Insurance_family")
  temp.d <- data.frame (new.d, g6_2)  
  result<-questionr::freq(temp.d$g6_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "G6_2. Insurance provided by another family member (e.g., spouse) through their current or former employer or union (including Kaiser/HMO/PPO)")
G6_2. Insurance provided by another family member (e.g., spouse) through their current or former employer or union (including Kaiser/HMO/PPO)
n % val%
Insurance_family 675 11.6 100
NA 5164 88.4 NA
Total 5839 100.0 100 
  g6_3 <- as.factor(d[,"g6_3"])
  levels(g6_3) <- list(Insurance_insurance_company="1")
  new.d <- data.frame(new.d, g6_3)
  new.d <- apply_labels(new.d, g6_3 = "Insurance_insurance_company")
  temp.d <- data.frame (new.d, g6_3)  
  result<-questionr::freq(temp.d$g6_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "G6_3. Insurance purchased directly from an insurance company (by you or another family member)")
G6_3. Insurance purchased directly from an insurance company (by you or another family member)
n % val%
Insurance_insurance_company 386 6.6 100
NA 5453 93.4 NA
Total 5839 100.0 100 
  g6_4 <- as.factor(d[,"g6_4"])
  levels(g6_4) <- list(Insurance_exchange="1")
  new.d <- data.frame(new.d, g6_4)
  new.d <- apply_labels(new.d, g6_4 = "Insurance_exchange")
  temp.d <- data.frame (new.d, g6_4)  
  result<-questionr::freq(temp.d$g6_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "G6_4. Insurance purchased from an exchange (sometimes called Obamacare or the Affordable Care Act)")
G6_4. Insurance purchased from an exchange (sometimes called Obamacare or the Affordable Care Act)
n % val%
Insurance_exchange 161 2.8 100
NA 5678 97.2 NA
Total 5839 100.0 100 
  g6_5 <- as.factor(d[,"g6_5"])
  levels(g6_5) <- list(Medicaid_state="1")
  new.d <- data.frame(new.d, g6_5)
  new.d <- apply_labels(new.d, g6_5 = "Medicaid_state")
  temp.d <- data.frame (new.d, g6_5)  
  result<-questionr::freq(temp.d$g6_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "G6_5. Medicaid or other state provided insurance")
G6_5. Medicaid or other state provided insurance
n % val%
Medicaid_state 788 13.5 100
NA 5051 86.5 NA
Total 5839 100.0 100 
  g6_6 <- as.factor(d[,"g6_6"])
  levels(g6_6) <- list(Medicare_government="1")
  new.d <- data.frame(new.d, g6_6)
  new.d <- apply_labels(new.d, g6_6 = "Medicare_government")
  temp.d <- data.frame (new.d, g6_6)  
  result<-questionr::freq(temp.d$g6_6,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "G6_6. Medicare/government insurance")
G6_6. Medicare/government insurance
n % val%
Medicare_government 2688 46 100
NA 3151 54 NA
Total 5839 100 100 
  g6_7 <- as.factor(d[,"g6_7"])
  levels(g6_7) <- list(VA_Military="1")
  new.d <- data.frame(new.d, g6_7)
  new.d <- apply_labels(new.d, g6_7 = "VA_Military")
  temp.d <- data.frame (new.d, g6_7)  
  result<-questionr::freq(temp.d$g6_7,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "G6_7. VA/Military Facility (including those who have ever used or enrolled for VA health care)")
G6_7. VA/Military Facility (including those who have ever used or enrolled for VA health care)
n % val%
VA_Military 919 15.7 100
NA 4920 84.3 NA
Total 5839 100.0 100 
  g6_8 <- as.factor(d[,"g6_8"])
  levels(g6_8) <- list(Do_not_have="1")
  new.d <- data.frame(new.d, g6_8)
  new.d <- apply_labels(new.d, g6_8 = "Do_not_have")
  temp.d <- data.frame (new.d, g6_8)  
  result<-questionr::freq(temp.d$g6_8,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "G6_8. I do not have any medical insurance")
G6_8. I do not have any medical insurance
n % val%
Do_not_have 96 1.6 100
NA 5743 98.4 NA
Total 5839 100.0 100

G7: Income

  • G7. What is your best estimate of your TOTAL FAMILY INCOME from all sources, before taxes, in the last calendar year? “Total family income” refers to your income PLUS the income of all family members living in this household (including cohabiting partners, and armed forces members living at home). This includes money from pay checks, government benefit programs, child support, social security, retirement funds, unemployment benefits, and disability.
    • 1=Less than $15,000
    • 2=$15,000 to $35,999
    • 3=$36,000 to $45,999
    • 4=$46,000 to $65,999
    • 5=$66,000 to $99,999
    • 6=$100,000 to $149,999
    • 7=$150,000 to $199,999
    • 8= $200,000 or more
  g7 <- as.factor(d[,"g7"])
  # Make "*" to NA
g7[which(g7=="*")]<-"NA"
  levels(g7) <- list(Less_than_15000="1",
                     Between_15000_35999="2",
                     Between_36000_45999="3",
                     Between_46000_65999="4",
                     Between_66000_99999="5",
                     Between_100000_149999= "6",
                     Between_150000_199999="7",
                     More_than_200000="8")
  g7 <- ordered(g7, c("Less_than_15000","Between_15000_35999","Between_36000_45999","Between_46000_65999","Between_66000_99999","Between_100000_149999", "Between_150000_199999","More_than_200000"))

  new.d <- data.frame(new.d, g7)
  new.d <- apply_labels(new.d, g7 = "income")
  temp.d <- data.frame (new.d, g7)  
  
  result<-questionr::freq(temp.d$g7,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g7")
g7
n % val% %cum val%cum
Less_than_15000 756 12.9 14.0 12.9 14.0
Between_15000_35999 967 16.6 17.9 29.5 31.9
Between_36000_45999 569 9.7 10.5 39.3 42.5
Between_46000_65999 776 13.3 14.4 52.5 56.8
Between_66000_99999 968 16.6 17.9 69.1 74.8
Between_100000_149999 753 12.9 14.0 82.0 88.7
Between_150000_199999 348 6.0 6.4 88.0 95.2
More_than_200000 260 4.5 4.8 92.4 100.0
NA 442 7.6 NA 100.0 NA
Total 5839 100.0 100.0 100.0 100.0

G8: # people supported by income

  • G8. In the last calendar year, how many people, including yourself, were supported by your family income?
    • 1=1
    • 2=2
    • 3=3
    • 4=4
    • 5=5 or more
  g8 <- as.factor(d[,"g8"])
  # Make "*" to NA
g8[which(g8=="*")]<-"NA"
  levels(g8) <- list(One="1",
                     Two="2",
                     Three="3",
                     Four="4",
                     Five_or_more="5")
  g8 <- ordered(g8, c("One","Two","Three","Four","Five_or_more"))

  new.d <- data.frame(new.d, g8)
  new.d <- apply_labels(new.d, g8 = "people supported by income")
  temp.d <- data.frame (new.d, g8)  
  
  result<-questionr::freq(temp.d$g8,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g8")
g8
n % val% %cum val%cum
One 1696 29.0 30.5 29.0 30.5
Two 2464 42.2 44.3 71.2 74.7
Three 746 12.8 13.4 84.0 88.1
Four 399 6.8 7.2 90.9 95.3
Five_or_more 261 4.5 4.7 95.3 100.0
NA 273 4.7 NA 100.0 NA
Total 5839 100.0 100.0 100.0 100.0

G9: Worry about finance

  • G9. How worried were you or your family about being able to pay your normal monthly bills, including rent, mortgage, and/or other costs:
      1. During young adult life (up to age 30):
      1. Age 31 (up to just before prostate cancer diagnosis):
      1. Current (from prostate cancer diagnosis to present):
      • 1=Not at all worried
      • 2=A little worried
      • 3=Somewhat worried
      • 4=Very worried
  g9a <- as.factor(d[,"g9a"])
  # Make "*" to NA
g9a[which(g9a=="*")]<-"NA"
  levels(g9a) <- list(Not_worried="1",
                      A_little_worried="2",
                      Somewhat_worried="3",
                      Very_worried="4")
  new.d <- data.frame(new.d, g9a)
  new.d <- apply_labels(new.d, g9a = "young adult life")
  temp.d <- data.frame (new.d, g9a)  
  result<-questionr::freq(temp.d$g9a,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "a. During young adult life (up to age 30)")
a. During young adult life (up to age 30)
n % val%
Not_worried 2684 46.0 47.4
A_little_worried 1474 25.2 26.0
Somewhat_worried 990 17.0 17.5
Very_worried 512 8.8 9.0
NA 179 3.1 NA
Total 5839 100.0 100.0 
  g9b <- as.factor(d[,"g9b"])
    # Make "*" to NA
g9b[which(g9b=="*")]<-"NA"
  levels(g9b) <- list(Not_worried="1",
                      A_little_worried="2",
                      Somewhat_worried="3",
                      Very_worried="4")
  new.d <- data.frame(new.d, g9b)
  new.d <- apply_labels(new.d, g9b = "age 31 up to before dx")
  temp.d <- data.frame (new.d, g9b)  
  result<-questionr::freq(temp.d$g9b,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "b. Age 31 (up to just before prostate cancer diagnosis)")
b. Age 31 (up to just before prostate cancer diagnosis)
n % val%
Not_worried 2824 48.4 51.2
A_little_worried 1504 25.8 27.3
Somewhat_worried 855 14.6 15.5
Very_worried 333 5.7 6.0
NA 323 5.5 NA
Total 5839 100.0 100.0 
  g9c <- as.factor(d[,"g9c"])
    # Make "*" to NA
g9c[which(g9c=="*")]<-"NA"
  levels(g9c) <- list(Not_worried="1",
                      A_little_worried="2",
                      Somewhat_worried="3",
                      Very_worried="4")
  new.d <- data.frame(new.d, g9c)
  new.d <- apply_labels(new.d, g9c = "current")
  temp.d <- data.frame (new.d, g9c)  
  result<-questionr::freq(temp.d$g9c,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "c. Current (from prostate cancer diagnosis to present)")
c. Current (from prostate cancer diagnosis to present)
n % val%
Not_worried 3158 54.1 56.7
A_little_worried 1122 19.2 20.2
Somewhat_worried 756 12.9 13.6
Very_worried 530 9.1 9.5
NA 273 4.7 NA
Total 5839 100.0 100.0

G10:Own or rent a house

  • G10. Is the home you live in:
    • 1=Owned or being bought by you (or someone in the household)?
    • 2=Rented for money?
    • 3=Other
  g10 <- as.factor(d[,"g10"])
  # Make "*" to NA
g10[which(g10=="*")]<-"NA"
  levels(g10) <- list(Owned="1",
                     Rented="2",
                     Other="3")
  g10 <- ordered(g10, c("Owned","Rented","Other"))

  new.d <- data.frame(new.d, g10)
  new.d <- apply_labels(new.d, g10 = "Own or rent a house")
  temp.d <- data.frame (new.d, g10)  
  
  result<-questionr::freq(temp.d$g10,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g10")
g10
n % val% %cum val%cum
Owned 4178 71.6 74.1 71.6 74.1
Rented 1310 22.4 23.2 94.0 97.3
Other 151 2.6 2.7 96.6 100.0
NA 200 3.4 NA 100.0 NA
Total 5839 100.0 100.0 100.0 100.0

G10 Other: Own or rent a house

g10other <- d[,"g10other"]
  new.d <- data.frame(new.d, g10other)
  new.d <- apply_labels(new.d, g10other = "g10other")
  temp.d <- data.frame (new.d, g10other)
result<-questionr::freq(temp.d$g10other, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "G10 Other")
G10 Other
n % val%
#NAME? 1 0.0 0.5
700 1 0.0 0.5
A friend 1 0.0 0.5
Agency 1 0.0 0.5
Air property family owned 1 0.0 0.5
Apartment 3 0.1 1.4
Apartment manager 1 0.0 0.5
Apartment rent 1 0.0 0.5
Apartment renting 1 0.0 0.5
Apartment. 4 0.1 1.9
Apt 1 0.0 0.5
Apt building 1 0.0 0.5
Apt.. 1 0.0 0.5
Assisted living 1 0.0 0.5
Bought. 1 0.0 0.5
budget inn 1 0.0 0.5
Buying 1 0.0 0.5
co-op 1 0.0 0.5
Cooperative 1 0.0 0.5
Currently in a nursing facility. 1 0.0 0.5
Currently live in a trailer on family land 1 0.0 0.5
Currently looking to move to apartment/townhouse/rent. 1 0.0 0.5
Daughter 1 0.0 0.5
Estate 1 0.0 0.5
Family 1 0.0 0.5
Family estate 2 0.0 1.0
Family home 5 0.1 2.4
Family home, pay rent. 1 0.0 0.5
Family home. 2 0.0 1.0
Family house 2 0.0 1.0
Family owned 1 0.0 0.5
Family property 3 0.1 1.4
Foreclosure 1 0.0 0.5
Foreclosure. 1 0.0 0.5
Free rent for now - friend. 1 0.0 0.5
Friend who he lives with owns it and he pays rent 1 0.0 0.5
Have a landlord 1 0.0 0.5
His house 1 0.0 0.5
Homeless 3 0.1 1.4
House belongs to my wife, Mrs. —- this home is permanent. Thank God. 1 0.0 0.5
House fire necessitated a rental home till home is repaired. 1 0.0 0.5
House sitter 1 0.0 0.5
HUD 1 0.0 0.5
I currently rent an apartment 1 0.0 0.5
I have a room in this house 1 0.0 0.5
I live in a RV park on the street 1 0.0 0.5
I live on Social Security only 1 0.0 0.5
I live with a friend 1 0.0 0.5
I live with a friend who I help out. 1 0.0 0.5
I live with my mother in her home. 1 0.0 0.5
I own 1 0.0 0.5
I owned the house 1 0.0 0.5
I pay rent 2 0.0 1.0
I pay rent for a room 1 0.0 0.5
I pay rent where I live 1 0.0 0.5
I rent 2 0.0 1.0
I rent (pay rent) 1 0.0 0.5
I rent a furnished room and kitchen priv. 1 0.0 0.5
I rent an apartment 1 0.0 0.5
I rent an apt.. 1 0.0 0.5
I rent here in Los Angeles and own a home in Neveda 1 0.0 0.5
I rent home from daughter. 1 0.0 0.5
I rent my house from owner. 1 0.0 0.5
I rent the home 1 0.0 0.5
I rent. 2 0.0 1.0
I rented 1 0.0 0.5
I worked and live on property for my boss. 1 0.0 0.5
I’m renting an apartment 1 0.0 0.5
If you need to call me Larry D. Joyner 912-344-5895. 1 0.0 0.5
Inherit it. 1 0.0 0.5
Inherited 1 0.0 0.5
Inherited. 1 0.0 0.5
It was paid for with Harvey and Borrowed from SBA to repair 1 0.0 0.5
James Williams. 1 0.0 0.5
Landlord 1 0.0 0.5
Lease 1 0.0 0.5
Leased 1 0.0 0.5
Live assisted living 1 0.0 0.5
Live in an apartment 1 0.0 0.5
Live in apartment community 1 0.0 0.5
Live in apartment complex 1 0.0 0.5
Live in apartment pay rent 1 0.0 0.5
Live in low income. 1 0.0 0.5
Live in my brothers house. 1 0.0 0.5
Live with family. 1 0.0 0.5
Live with mother in law 1 0.0 0.5
Live with parents 1 0.0 0.5
living in car/homeless 1 0.0 0.5
Living with a family member. 1 0.0 0.5
Living with cousin’s home 1 0.0 0.5
Living with uncle 1 0.0 0.5
Lost home in tax sale. 1 0.0 0.5
Low income senior citizens 1 0.0 0.5
Mobile Home Camper 1 0.0 0.5
Mortgage 4 0.1 1.9
Mortgage. 1 0.0 0.5
Mother 1 0.0 0.5
Mother. 1 0.0 0.5
My mothers 1 0.0 0.5
My sister’s house. 1 0.0 0.5
My sisters home 1 0.0 0.5
My wife and myself owned. 1 0.0 0.5
No mortgage 1 0.0 0.5
No my home 1 0.0 0.5
Nursing home 1 0.0 0.5
Own 2 0.0 1.0
Owned 3 0.1 1.4
Owned —- out to 1985, zero love dollars and affection 1 0.0 0.5
Owned by mother. 1 0.0 0.5
Owned by my employer 1 0.0 0.5
Owned by my wife 1 0.0 0.5
Owned by parent, paying rent, car note 1 0.0 0.5
owned by sister 1 0.0 0.5
Owned by someone in household. 1 0.0 0.5
Owned by Step daughter 1 0.0 0.5
Owned house 1 0.0 0.5
Owned. 2 0.0 1.0
Paid for 2 0.0 1.0
Paid in full. 1 0.0 0.5
Passed down to me 1 0.0 0.5
Pay association fees 1 0.0 0.5
Pay for! 1 0.0 0.5
Pay rent. 1 0.0 0.5
Paying mortgage 1 0.0 0.5
Paying mortgage. 1 0.0 0.5
Paying rent 1 0.0 0.5
Provided by my employer 1 0.0 0.5
Public housing. 1 0.0 0.5
Pvt owned 1 0.0 0.5
Rent 6 0.1 2.9
Rent a room 2 0.0 1.0
Rent an apartment 1 0.0 0.5
Rent apt 1 0.0 0.5
Rent apt.. 1 0.0 0.5
Rent from ex wife 1 0.0 0.5
Rent I pay 200 month 1 0.0 0.5
Rent room 1 0.0 0.5
Rent space. 1 0.0 0.5
Rent. 2 0.0 1.0
Rental 1 0.0 0.5
rental - apartment 1 0.0 0.5
rented 1 0.0 0.5
Rented 2 0.0 1.0
Rented home 1 0.0 0.5
renter 1 0.0 0.5
Renting 2 0.0 1.0
Renting a home 1 0.0 0.5
Reverse mortgage 2 0.0 1.0
Rooming 1 0.0 0.5
Roommate situation. 1 0.0 0.5
RV 1 0.0 0.5
Senior apt 1 0.0 0.5
Senior citizen building (Brookside Apartments). 1 0.0 0.5
Senior citizen housing 1 0.0 0.5
Senior facility. 1 0.0 0.5
Senior living qtr.. 1 0.0 0.5
Senior Public Housing 1 0.0 0.5
Seniors home 1 0.0 0.5
Sisters house 1 0.0 0.5
Son owns the home-I pay the mortgage. 1 0.0 0.5
Squatted. 1 0.0 0.5
Staying with a friend. 1 0.0 0.5
Staying with my daughter and family 1 0.0 0.5
Staying with relative who owns the home 1 0.0 0.5
This house is hers-not my house. Renter. 1 0.0 0.5
Trailer-living with family. 1 0.0 0.5
we are selling home 1 0.0 0.5
Wife 2 0.0 1.0
Wife family home. 1 0.0 0.5
Work compensation 1 0.0 0.5
work for the us government 1 0.0 0.5
NA 5630 96.4 NA
Total 5839 100.0 100.0

G11:Lose current sources

  • G11. If you lost all your current source(s) of household income (your paycheck, public assistance, or other forms of income), how long could you continue to live at your current address and standard of living?
    • 1=Less than 1 month
    • 2=1 to 2 months
    • 3=3 to 6 months
    • 4=More than 6 months
  g11 <- as.factor(d[,"g11"])
  # Make "*" to NA
g11[which(g11=="*")]<-"NA"
  levels(g11) <- list(Less_than_1_month="1",
                     One_to_two_month="2",
                     Three_to_six_month="3",
                     More_than_6_months="4")
  g11 <- ordered(g11, c("Less_than_1_month","One_to_two_month","Three_to_six_month","More_than_6_months"))

  new.d <- data.frame(new.d, g11)
  new.d <- apply_labels(new.d, g11 = "ose current sources")
  temp.d <- data.frame (new.d, g11)  
  
  result<-questionr::freq(temp.d$g11,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g11")
g11
n % val% %cum val%cum
Less_than_1_month 688 11.8 12.5 11.8 12.5
One_to_two_month 1083 18.5 19.7 30.3 32.2
Three_to_six_month 1201 20.6 21.8 50.9 54.1
More_than_6_months 2525 43.2 45.9 94.1 100.0
NA 342 5.9 NA 100.0 NA
Total 5839 100.0 100.0 100.0 100.0

G12: Today’s date

  • G12. Please enter today’s date.
  g12 <- as.Date(d[ , "g12"], format="%m/%d/%y")
  new.d <- data.frame(new.d, g12)
  new.d <- apply_labels(new.d, g12 = "today’s date")
  #temp.d <- data.frame (new.d.1, g12) 
  
  summarytools::view(dfSummary(new.d$g12, style = 'grid',
                               max.distinct.values = 5, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 g12 [labelled, Date] today’s date
min : 1980-08-23
med : 2020-06-25
max : 2040-11-14
range : 60y 2m 22d
 402 distinct values 79 (1.4%)

Generated by summarytools 1.0.0 (R version 3.6.3)
2021-12-09